README.md

S2FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity

S2FT offers several key advantages over LoRA: (i) improved OOD performance, (2) enhanced training efficiency (time & memory), (3) better serving scalability (adapter fusion/switch/parallelism). These features are particularly valuable in real-world PEFT scenarios, where the goal is to effectively combine knowledge from various domains with the base model's capabilities using limited resource.

Installation

Install S2FT Layers

python setup.py install

Install experiment dependencies

pip install -r requirements.txt

Usage

Here, we demonstrate how to apply S2FT in training with the LLaMA architecture. See advanced usage in experiments/train/finetune.py (Line 266-330) and experiments/utils/s2_utils.py.

Step 1: Import two basic S2 linear layers

from s2ft import S2ColumnLinear, S2RowLinear

Step 2: Allocate trainable rows/columns to each linear layer

parameters_d = {}
intermediate_dim = model.config.intermediate_size
ffn_indices = [
    i for i in range(intermediate_dim * model.config.num_hidden_layers)
]
for i in range(model.config.num_hidden_layers):
    parameters_d[i] = []
num_d = int(intermediate_dim * model.config.num_hidden_layers * args.d_ratio)
select_d = sorted(random.sample(ffn_indices, num_d))
for d in select_d:
    parameters_d[d // intermediate_dim].append(d % intermediate_dim)
selected_parameters = {"down_proj": parameters_d}

Step 3: Convert linear layers to S2 linear layers

def convert_ffn_layer_to_s2(model, selected_parameters):
    for i in range(model.config.num_hidden_layers):
        layer = model.model.layers[i]
        order = selected_parameters["down_proj"][i]
        for j in range(model.config.intermediate_size):
            if j not in order:
                order.append(j)
        
        module = layer.mlp.down_proj
        checkpoint = copy.deepcopy(module.state_dict())
        layer.mlp.down_proj = S2RowLinear(
            in_features=module.in_features,
            out_features=module.out_features,
            bias=module.bias,
            start=len(only_u),
            end=(len(only_u) + len(ud) + len(only_d)),
            device=next(module.parameters()).device,
            dtype=next(module.parameters()).dtype,
        )
        layer.mlp.down_proj.load_state_dict(checkpoint, strict=False)
        del module
        del checkpoint

        u_weight = layer.mlp.up_proj.weight.data
        layer.mlp.up_proj.weight.data = u_weight[order, :]
        g_weight = layer.mlp.gate_proj.weight.data
        layer.mlp.gate_proj.weight.data = g_weight[order, :]
        d_weight = layer.mlp.down_proj.weight.data
        layer.mlp.down_proj.weight.data = d_weight[:, order]

Experiments

To reproduce S2FT results on the commonsense and arithmetic reasoning dataset, refer to the instruction and code available in the experiments directory.

For the efficient implementation of S2FT and the related efficiency analysis code, see the efficiency directory.

Citation

@inproceedings{yang2024s2ft,
  title={S2FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity},
  author={Yang, Xinyu and Leng, Jixuan and Guo, Geyang and Zhao, Jiawei and Nakada, Ryumei and Zhang, Linjun and Yao, Huaxiu and Chen, Beidi},
  booktitle={The 38th Conference on Neural Information Processing Systems (NeurIPS)},
  year={2024}
}