| import argparse |
| from model_utils import get_llama, llama_sparsellm, llama_eval |
| from datautils import get_loaders |
| import torch |
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
|
|
| |
| parser.add_argument("--model", type=str, default='meta-llama/Llama-2-7b-hf', help="LLaMA model to load") |
| parser.add_argument("--dataset", type=str, choices=["wikitext2", "ptb", "c4"], default="c4", help="Dataset for calibration.") |
| parser.add_argument("--seed", type=int, default=0, help="Seed for sampling calibration data.") |
| parser.add_argument("--nsamples", type=int, default=32, help="Number of calibration data samples.") |
| parser.add_argument("--percdamp", type=float, default=0.01, help="Percent of Hessian diagonal for dampening.") |
| parser.add_argument("--sparsity", type=float, default=0.5, help="Target sparsity.") |
| parser.add_argument("--prunen", type=int, default=0, help="N for N:M pruning.") |
| parser.add_argument("--prunem", type=int, default=0, help="M for N:M pruning.") |
| parser.add_argument("--blocksize", type=int, default=128, help="Blocksize for adaptive mask selection.") |
| parser.add_argument("--gmp", action="store_true", help="Run GMP baseline.") |
| parser.add_argument("--wbits", type=int, default=16, help="Quantization bits.") |
| parser.add_argument("--minlayer", type=int, default=-1, help="Prune layers with id >= this.") |
| parser.add_argument("--maxlayer", type=int, default=1000, help="Prune layers with id < this.") |
| parser.add_argument("--prune_only", type=str, default="", help="Prune only layers containing this text.") |
| parser.add_argument("--invert", action="store_true", help="Invert subset.") |
| parser.add_argument("--save", type=str, default="", help="Path to save model.") |
| parser.add_argument("--true-sequential", action="store_true", help="Run in true sequential mode.") |
| parser.add_argument("--log_wandb", action="store_true", help="Log to W&B.") |
|
|
| args = parser.parse_args() |
|
|
| model = get_llama(args) |
| model.eval() |
| dataloader, testloader = get_loaders(args.dataset, nsamples=args.nsamples, seed=args.seed, model=args.model, seqlen=model.seqlen) |
|
|
| if (args.sparsity or args.prunen) and not args.gmp: |
| llama_sparsellm(model, dataloader, torch.device('cuda'), args) |
|
|
| for dataset in ['wikitext2', 'ptb', 'c4']: |
| dataloader, testloader = get_loaders(dataset, seed=args.seed, model=args.model, seqlen=model.seqlen) |
| llama_eval(model, testloader, torch.device('cuda'), args, dataset) |
|
|
| if args.save: |
| model.save_pretrained(args.save) |
|
|
| if __name__ == '__main__': |
| main() |
|
|
