shears-mpt-7b-50-gsm8k-super-adapter

Shears Adapter Card: shears-mpt-7b-50-gsm8k-super-adapter

The super-adapter-network fine-tuned on sparsified MPT-7B with GSM8K datasets using Shears.

The release of the super-network is to facilitate users to apply their own search algorithms and evaluation indicators to extract subnetworks suitable for their specific needs.

Paper Abstract

Recently, several approaches successfully demonstrated that weight-sharing Neural Architecture Search (NAS) can effectively explore a search space of elastic low-rank adapters (LoRA), allowing the parameter-efficient fine-tuning (PEFT) and compression of large language models. In this paper, we introduce a novel approach called Shears, demonstrating how the integration of cost-effective sparsity and a proposed Neural Low-rank adapter Search (NLS) algorithm can further improve the efficiency of PEFT approaches. Results demonstrate the benefits of Shears compared to other methods, reaching high sparsity levels while improving or with little drop in accuracy, utilizing a single GPU for a pair of hours.

Model Details

Information

• Adapter name: shears-mpt-7b-50-gsm8k-super-adapter
• Base model: IntelLabs/shears-mpt-7b-50-base
• Sparsity: 50%
• Subnetwork version: Super
• NNCF Configuration: nncf_shears_mpt.json

Adapter Configuration

• LoRA rank: 32
• LoRA alpha: 64
• LoRA target modules: q_proj, k_proj, v_proj, out_proj, up_proj, down_proj
• LoRA rank search space: [32, 24, 16] (for each LoRA module)

Training Hyperparameters

• Batch size: 16
• Learning rate: 3e-4
• Epoch: 5

Training and Evaluation

GSM8K dataset: https://huggingface.co/datasets/gsm8k

How to use

Refer to the illustrative example provided in load_and_explore_supernet.ipynb for a comprehensive understanding. This notebook shows the direct loading of a Shears super-network and the extraction of diverse subnetworks from it. This feature empowers users to employ their own search algorithms and evaluation metrics for the extraction of subnetworks customized to their specific requirements.

Moreover, the super-network is essentially the maximal subnetwork, and it can also be directly loaded:

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer

def generate_prompt(instruction):
    return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. 

                    ### Instruction:
                    {instruction}

                    ### Response:
                    """

base_model = AutoModelForCausalLM.from_pretrained("IntelLabs/shears-mpt-7b-50-base", trust_remote_code=True)
model = PeftModel.from_pretrained(base_model, "IntelLabs/shears-mpt-7b-50-gsm8k-super-adapter")
model.eval()

non_zero_params = sum([(param.data != 0).sum().item() for _, param in model.named_parameters()])
print(f"Number of all non-zero parameters: {non_zero_params}")

tokenizer = AutoTokenizer.from_pretrained("IntelLabs/shears-mpt-7b-50-base", trust_remote_code=True)

instruction = "Edgar eats 18 pretzels a day. If his brother eats 1/2 as many, how many does his brother eat in a week?"
prompt = generate_prompt(instruction)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(model.device)
with torch.no_grad():
    generation_output = model.generate(
        input_ids=input_ids,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=256,
        use_cache=True,
        num_beams=4,
    )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
print(output)

Evaluation Results

Results of the heuristic sub-network discoverd from the super-network:

Model Sources

Repository: https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears

Paper:

• Shears: Unstructured Sparsity with Neural Low-rank Adapter Search
• Low-Rank Adapters Meet Neural Architecture Search for LLM Compression

Ethical Considerations

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.

Citation

@inproceedings{munoz-etal-2024-shears,
    title = "Shears: Unstructured Sparsity with Neural Low-rank Adapter Search",
    author = "Mu{\~n}oz, J. Pablo  and
      Yuan, Jinjie  and
      Jain, Nilesh",
    editor = "Yang, Yi  and
      Davani, Aida  and
      Sil, Avi  and
      Kumar, Anoop",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-industry.34",
    doi = "10.18653/v1/2024.naacl-industry.34",
    pages = "395--405",
}

License

Apache-2.0