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cse517-final-project

In this project, we study whether ordering the data beased on various "hardness" metrics affects the accuracy and efficiency of training.

Environment Setup

Finetuning and evaluating the TinyLlama Model on SNLI and GSM8k data requires certain packages. The requirements.txt file lists what is needed.

For conda environment setup:

conda env create -f environment.yml

For venv environment setup (on VM) (see Google Cloud docs), or anywhere else Python and/or venv might not yet be available:

sudo apt update
sudo apt install python3 python3-dev python3-venv
sudo apt-get install wget
wget https://bootstrap.pypa.io/get-pip.py
sudo python3 get-pip.py

Then, create venv environment, activate, and install all requirements:

cd <project>
python3 -m venv nlp-project

source nlp-project/bin/activate
pip install -r requirements.txt

Finetuning and Evaluating TinyLlama

Scripts for training and evaluating TinyLlama can be found under scripts/training. To set up arguments, create a config.json file with all training/evaluation parameters. Here is an example of a valid config to train using S-Loss with the confidence metric:

{
    "repo_path": "/path/to/cse517-final-project",
    "model_name": "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
    "hardness_calc": "confidence",
    "loss_calc": "triangle",
    "data_order": null,
    "architecture_args": {
        "unfrozen_layers": [
            "score"
        ],
        "quantized": null
    },
    "dataset": "snli",
    "dataset_coordinates": "data/snli_data_map_coordinates.pickle",
    "dataset_ngram_path": "data/interpolated_ngram_perplexity.jsonl",
    "num_train_samples": 50000,
    "num_val_samples": 5000,
    "num_test_samples": 5000,
    "trainer_args": {
        "optim": "adamw_torch",
        "adam_beta1": 0.9,
        "adam_beta2": 0.95,
        "weight_decay": 0.01,
        "learning_rate": 4e-4,
        "lr_scheduler_type": "constant",
        "num_warmup_steps": 0,
        "num_train_epochs": 2,
        "batch_size": 8,
        "dataloader_num_workers": 8,
        "load_best_model_at_end": true,
        "strategy": "epoch",
        "output_dir": "scripts/experiment_data/",
        "eval_every": 200
    },
    "do_train": true
}

Then pass the path to this configuration file as the first argument to the train_parallel.py:

export CUDA_VISIBLE_DEVICES=0,1,2,3
srun accelerate launch --multi_gpu --num_processes=4 --num_machines 1 --mixed_precision 'no' --dynamo_backend 'no' ./scripts/training/train_parallel.py --config_path="./scripts/training/config.json"

Setting do_train=False will skip training and will only evaluate metrics on the validation and test sets (sampled with num_val_samples and num_test_samples from the validation and test samples of the provided dataset name (dataset is loaded from HF)).