This repo shows the implementation of the MoLA parameter-efficient tuning in the paper Higher Layers Need More LoRA Experts.
The installation is for Linux
- Clone this repository and navigate to MoLA folder
git clone https://github.com/GCYZSL/MoLA.git cd MoLA - Install dependencies
conda create -n mola python=3.10 -y conda activate mola pip install -r requirements.txt
We take ScienceQA as an example, which can be applied to customized datasets as well.
python preparation_scienceqa_data.py \
--save_path "./scienceqa"This script takes the HuggingFace dataset as input and processes the data sample following the format and code at mm-cot.
Note: We use the HuggingFace datasets library to load the ScienceQA dataset from HuggingFace Hub. If you want to load datasets from local files or other methods, please refer to this tutorial to modify the script accordingly. In most cases, you only need to modify line 110 as in the preparation_scienceqa_data.py:
datasets_scienceqa = load_dataset(args.dataset) # Modify this line to load datasets in different formatThe processed data sample should contain four essential components instruction, input, and output are used for downstream task instruction tuning. answer is used in evaluation only.
| instruction | The question and the choices. (Question: ...? Options: (A) ... (B) ...) |
| input | Not used in this situation. |
| output | The answer (Answer: The answer is B.) |
| answer | The answer to the question which is used for evaluation. (B) |
The output of the scripts contains one Huggingface dataset (science_qa.hf) for training and three JSON files. The scienceq_test.json is for the evaluation. scienceq_train.json and scienceq_validation.json are not necessary.
├── scienceqa
│ └── science_qa.hf
│ └── scienceq_train.json
│ └── scienceq_validation.json
│ └── scienceq_test.json
The input data for the training script is the science_qa.hf that we processed in the previous step.
We briefly introduce the important hyperparameters as follows:
| Hyperparameters | |
|---|---|
| base_model | The base model that we used. We use the model provided by Huggingface. (We only support LLaMA series) |
| data_path | The path of science_qa.hf |
| batch_size/micro_batch_size | The micro_batch_size should be less than batch_size. |
| num_epochs/learning_rate | Training settings |
| lora_r/lora_alpha/lora_dropout | Each expert's LoRA settings |
| lora_target_modules | The modules applied to our MoLA, which can be chosen from (q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj) and should be separated by comma. |
| number_experts | The number of experts for each layer, which contains 32 numbers (2,2,2,2,2,2,2,2,4,4,4,4,4,4,4,4,6,6,6,6,6,6,6,6,8,8,8,8,8,8,8,8) |
| top_k | The top K value for each layer, which contains 32 numbers (2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2) |
| resume_from_checkpoint | Path of the trained MoLA model used for continuous training. |
| obalance | The usage of balance loss. |
Training on sample data:
python mola_training.py \
--base_model "NousResearch/Llama-2-7b-hf" \
--data_path "./sampled_data/sampled_scienceqa_train_all.hf" \
--output_dir "./sampled_scienceqa_mola" \
--batch_size 128 \
--micro_batch_size 8 \
--num_epochs 1 \
--learning_rate 3e-4 \
--cutoff_len 256 \
--val_set_size 1 \
--lora_r "8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8" \
--lora_alpha 16 \
--lora_dropout 0.05 \
--lora_target_modules "q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj" \
--number_experts "2,2,2,2,2,2,2,2,4,4,4,4,4,4,4,4,6,6,6,6,6,6,6,6,8,8,8,8,8,8,8,8" \
--top_k "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2" \
--train_on_inputs \
--group_by_length \
--add_eos_token Training on ScienceQA data:
python mola_training.py \
--base_model "NousResearch/Llama-2-7b-hf" \
--data_path "./scienceqa/science_qa.hf" \
--output_dir "./scienceqa_mola" \
--batch_size 128 \
--micro_batch_size 8 \
--num_epochs 20 \
--learning_rate 3e-4 \
--cutoff_len 256 \
--val_set_size 1 \
--lora_r "8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8" \
--lora_alpha 16 \
--lora_dropout 0.05 \
--lora_target_modules "q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj" \
--number_experts "2,2,2,2,2,2,2,2,4,4,4,4,4,4,4,4,6,6,6,6,6,6,6,6,8,8,8,8,8,8,8,8" \
--top_k "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2" \
--train_on_inputs \
--group_by_length \
--add_eos_token We support inference for one sample and the parameters for text generation can be set, which includes temperature, top_p, and top_k_g.
A sample input text is
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Context: Hint: People who can knit had to learn how to do it.
Question: Is the following trait inherited or acquired?
Sasha is good at knitting hats.
Options: (A) acquired (B) inherited
### Response:Please run the mola_inference.ipynb.
We support the evaluation of batch samples.
python evaluation_scienceqa.py \
--test_dataset "./scienceqa/scienceq_test.json" \
--base_model "NousResearch/Llama-2-7b-hf" \
--mola_weights "./scienceqa_mola" \
--batch_size 8 \
--lora_target_modules "q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj" \
--number_experts "2,2,2,2,2,2,2,2,4,4,4,4,4,4,4,4,6,6,6,6,6,6,6,6,8,8,8,8,8,8,8,8" \
--top_k "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2" \
--save_path "./results/mola_test_sciqa.json"In our implementation, we set instruction and input to None, and output contains all text for pretraining.
python preparation_instruction_data.py \
--num_samples 50000 \
--save_path './meta_moe_50k.hf' \Training on Instruction data (Option):
python mola_training_instruction.py \
--base_model "NousResearch/Llama-2-7b-hf" \
--data_path "meta_moe_50k.hf" \
--output_dir "./meta_moe_llm2_3ep_2468" \
--batch_size 128 \
--micro_batch_size 8 \
--num_epochs 3 \
--learning_rate 3e-4 \
--cutoff_len 256 \
--val_set_size 1 \
--lora_r "8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8" \
--lora_alpha 16 \
--lora_dropout 0.05 \
--lora_target_modules "q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj" \
--number_experts "2,2,2,2,2,2,2,2,4,4,4,4,4,4,4,4,6,6,6,6,6,6,6,6,8,8,8,8,8,8,8,8" \
--top_k "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2" \
--train_on_inputs \
--add_eos_token The details of the hyperparameters can be found in Training of Quick Start.
Will be updated.
The code is developed based on Huggingface, mm-cot, and alpaca-lora projects.
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