# Configuring and running experiments
## Overview The large number of component variants supported in this repository creates the need for configuring many components and their parameters before running a specific experiment. We rely on features provided by Hydra to make this process easier. At the core, three main Hydra configs—`train.yaml` (generic training), `eval.yaml` (running evaluation), and `unlearn.yaml` (unlearning training)—provide the base configuration for the main types of experiments. These are then extended by experiment-specific configs and command-line overrides. We set up experiment configs for common usecases like LLaMA-2 unlearning on TOFU, LLaMA-2 evaluation on MUSE etc. which set the required datasets, models, and base train and eval configs to make things easier. Experiment output directories are constructed based on the task mode (`train` / `eval` / `unlearn`) and the task name (provided by the user) as `./saves/${mode}/${task_name}`. The experiment logging will display where the model checkpoints, logs and evaluation dumps are stored. --- ### Table of Contents - [Overview](#overview) - [Table of Contents](#table-of-contents) - [Example Commands](#example-commands) - [Commonly Overridden Arguments](#commonly-overridden-arguments) - [Model Settings](#model-settings) - [Trainer Settings](#trainer-settings) - [Data Settings](#data-settings) - [Experiment Settings](#experiment-settings) - [Simple Finetuning](#simple-finetuning) - [Distributed Training](#distributed-training) --- ## Example Commands ```bash ## runs a finetuning using experiment details from configs/finetune/tofu/default.yaml python src/train.py --config-name=train.yaml experiment=finetune/tofu/default task_name=SAMPLE_TRAIN ## runs an unlearning training using experiment details from configs/unlearn/tofu/default.yaml # output directory will be constructed as: saves/unlearn/SAMPLE_UNLEARN python src/train.py --config-name=unlearn.yaml experiment=unlearn/tofu/default task_name=SAMPLE_TRAIN ## runs an evaluation using experiment details from configs/eval/muse/default.yaml python src/eval.py --config-name=eval.yaml experiment=eval/muse/default task_name=SAMPLE_EVAL ## Note: eval.yaml is the default config set in src/eval.py, so this argument can be omitted ## an extensively filled out configuration for an unlearning experiment python src/train.py --config-name=unlearn.yaml experiment=unlearn/muse/default data_split=News \ trainer=NPO trainer.method_args.retain_loss_type=KL task_name=llama2_books_NPO_KL \ retain_logs_path=saves/eval/muse_books_retain/MUSE_EVAL.json ## an even more extensively filled out configuration for an unlearning experiment python src/train.py --config-name=unlearn.yaml \ experiment=unlearn/tofu/default.yaml \ task_name=NPO_unlearn_tofu_llama_8 \ model=Llama-3.1-8B-Instruct \ model.model_args.pretrained_model_name_or_path=saves/finetune/path_model_llama \ trainer=NPO trainer.args.per_device_train_batch_size=4 \ forget_split=forget05 retain_split=retain95 \ retain_logs_path=saves/eval/tofu_retain95/TOFU_EVAL.json \ paths.output_dir=saves/unlearn/NPO/evals ``` > [!NOTE] The unlearning experiments support evaluation during the unlearning finetuning. But this is supported only when a single accelerator process is used, checkpoints must be stored and evaluated after training. --- ## Commonly Overridden Arguments To understand the structure of an evaluation config and the kind of available parameters for overriding, refer to: [`configs/experiment/examples/tofu_eval.yaml`](../configs/experiment/examples/tofu_eval.yaml). To understand the structure of an unlearning config and the kind of available parameters for overriding, refer to: [`configs/experiment/examples/muse_unlearn.yaml`](../configs/experiment/examples/muse_unlearn.yaml). The following tables list the most commonly used arguments while running experiments. ###

Model Settings

Argument Description and examples
model Selecting the model. Example: model=Llama-2-7b-hf
model.model_args.pretrained_model_name_or_path Specifies the model checkpoint or HuggingFace ID.
model.tokenizer_args.pretrained_model_name_or_path Specifies the tokenizer location. Make sure this matches the model from above by providing model path as needed..
model.template_args Optional chat templating parameters (e.g., start/end tags). Example: apply_chat_template: false, user_start_tag: "[INST] "
###

Trainer Settings

Argument Description and examples
trainer Overall trainer or unlearning method selection, decides the finetuning algorithm. Example: trainer=NPO or trainer=finetune
trainer.args Main training hyperparameters like per_device_train_batch_size, per_device_eval_batch_size, gradient_accumulation_steps, learning_rate, num_train_epochs, optim and other HuggingFace TrainingArguments.
trainer.method_args Method-specific parameters for unlearning trainers. Example: retain_loss_type, NPO hyperparams like gamma, alpha, beta etc.
###

Data Settings

Argument Description and examples
data Overall data configuration/format. Example: data=unlearn, data=finetune.
data.forget, data.retain, data.anchor etc. Set sub-datasets in the overall dataset using data.forget=MUSE_forget data.retain=MUSE_retain, set which sub-dataset to index over (others are randomly sampled) using data.anchor=forget
data_split/forget_split/retain_split These arguments are custom to specific datasets and are used to populate dataset paths.
data_split specifies the overall dataset split or type. Example: data_split=News or data_split=Books
forget_split/retain_split splits are used to use various sub-parts of the dataset. Example: forget_split=forget01 retain_split=retain99
###

Experiment Settings

Argument Description and examples
task_name Experiment identifier used to generate custom output paths. Example: task_name=llama2_books_NPO_KL.
eval Overall evaluation benchmark configuration selection. Example: eval=muse.
retain_logs_path Path to load eval logs of retain models used some evaluation metrics Example: retain_logs_path=saves/eval/muse_books_retain/MUSE_EVAL.json.
paths Contains attributes used to decide path configuration like paths.output_dir=$LOCAL_PATH.
--- ## Simple Finetuning In addition to running unlearning based finetuning, we also support simple finetuning training with a given dataset. These use [`src/train.py`](../src/train.py) with the [`train.yaml`](../train.yaml) config to set up a standard supervised training environment. Parameters such as learning rate, batch size, and optimizer settings can be adjusted via experiment-specific configs or command-line overrides. Example: ```bash python src/train.py --config-name=train.yaml experiment=finetune/tofu/default \ trainer.args.learning_rate=5e-5 task_name=llama3.2-1B_finetune_example ``` ## Distributed Training Distributed training configurations enable scaling experiments across multiple devices or nodes. In most cases, default distributed settings from [`configs/accelerate/default_config.yaml`](../configs/accelerate/default_config.yaml) are sufficient. You can run distributed training with the below command that uses DeepSpeed for distributed training (which is our default setup): ```bash CUDA_VISIBLE_DEVICES=0,1 accelerate launch \ --config_file configs/accelerate/default_config.yaml --main_process_port 18765 \ src/train.py --config-name=unlearn.yaml experiment=unlearn/muse/default.yaml task_name=DISTRIBUTED_TRAIN ``` You may also simply run `CUDA_VISIBLE_DEVICES=0,1,.. python ...` to leverage Accelerate's DDP or model parallel. For model parallel you can use `device_map="auto"` in the `model_args` while loading the model. > [!CAUTION] > Train runs using multiple accelerate processes will not be able to run evaluations during training. To achieve this, you may want to use DDP/model parallel (see #94) or use a single GPU to run the evaluation code directly on a saved model checkpoint like below ```bash CUDA_VISIBLE_DEVICES=0 python src/eval.py experiment=eval/muse/default.yaml task_name=SAMPLE_EVAL \ model.model_args.pretrained_model_name_or_path=saves/unlearn/muse_unlearn_exp \ ```