WizardCoder: Empowering Code Large Language Models with Evol-Instruct

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[2024/01/04] 🔥 We released WizardCoder-33B-V1.1 trained from deepseek-coder-33b-base, the SOTA OSS Code LLM on EvalPlus Leaderboard, achieves 79.9 pass@1 on HumanEval, 73.2 pass@1 on HumanEval-Plus, 78.9 pass@1 on MBPP, and 66.9 pass@1 on MBPP-Plus.

[2024/01/04] 🔥 WizardCoder-33B-V1.1 outperforms ChatGPT 3.5, Gemini Pro, and DeepSeek-Coder-33B-instruct on HumanEval and HumanEval-Plus pass@1.

[2024/01/04] 🔥 WizardCoder-33B-V1.1 is comparable with ChatGPT 3.5, and surpasses Gemini Pro on MBPP and MBPP-Plus pass@1.

How to Make the Training Data?

Apply our Code Evol-Instruct on Code-Aplaca data.

❗ Data Contamination Check:

Before model training, we carefully and rigorously checked all the training data, and used multiple deduplication methods to verify and prevent data leakage on HumanEval and MBPP test set.

🔥 ❗Note for model system prompts usage:

Please use the same systems prompts strictly with us, and we do not guarantee the accuracy of the quantified versions.

Default version:

"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"

How to Reproduce the Performance of WizardCoder-33B-V1.1

We provide all codes here.

We also provide all generated results.

transformers==4.36.2
vllm==0.2.5

(1) HumanEval and HumanEval-Plus

• Step 1

Code Generation (w/o accelerate)

model="WizardLM/WizardCoder-33B-V1.1"
temp=0.0
max_len=2048
pred_num=1
num_seqs_per_iter=1

output_path=preds/T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode

mkdir -p ${output_path}
echo 'Output path: '$output_path
echo 'Model to eval: '$model

# 164 problems, 21 per GPU if GPU=8
index=0
gpu_num=8
for ((i = 0; i < $gpu_num; i++)); do
 start_index=$((i * 21))
 end_index=$(((i + 1) * 21))

 gpu=$((i))
 echo 'Running process #' ${i} 'from' $start_index 'to' $end_index 'on GPU' ${gpu}
 ((index++))
 (
 CUDA_VISIBLE_DEVICES=$gpu python humaneval_gen.py --model ${model} \
 --start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
 --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --greedy_decode
 ) &
 if (($index % $gpu_num == 0)); then wait; fi
done

Code Generation (w/ vllm accelerate)

model="WizardLM/WizardCoder-33B-V1.1"
temp=0.0
max_len=2048
pred_num=1
num_seqs_per_iter=1

output_path=preds/T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode_vllm

mkdir -p ${output_path}
echo 'Output path: '$output_path
echo 'Model to eval: '$model

CUDA_VISIBLE_DEVICES=0,1,2,3 python humaneval_gen_vllm.py --model ${model} \
 --start_index 0 --end_index 164 --temperature ${temp} \
 --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --num_gpus 4 --overwrite

• Step 2: Get the score

Install Eval-Plus benchmark.

git clone https://github.com/evalplus/evalplus.git
cd evalplus
export PYTHONPATH=$PYTHONPATH:$(pwd)
pip install -r requirements.txt

Get HumanEval and HumanEval-Plus scores.

output_path=preds/T0.0_N1_WizardCoder-33B-V1.1_Greedy_Decode

echo 'Output path: '$output_path
python process_humaneval.py --path ${output_path} --out_path ${output_path}.jsonl --add_prompt

evalplus.evaluate --dataset humaneval --samples ${output_path}.jsonl

(2) MBPP and MBPP-Plus

The preprocessed questions are provided in mbppplus.json.

• Step 1

Code Generation (w/o accelerate)

model="WizardLM/WizardCoder-33B-V1.1"
temp=0.0
max_len=2048
pred_num=1
num_seqs_per_iter=1

output_path=preds/MBPP_T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode

mkdir -p ${output_path}
echo 'Output path: '$output_path
echo 'Model to eval: '$model

# 399 problems, 50 per GPU if GPU=8
index=0
gpu_num=8
for ((i = 0; i < $gpu_num; i++)); do
 start_index=$((i * 50))
 end_index=$(((i + 1) * 50))

 gpu=$((i))
 echo 'Running process #' ${i} 'from' $start_index 'to' $end_index 'on GPU' ${gpu}
 ((index++))
 (
 CUDA_VISIBLE_DEVICES=$gpu python mbppplus_gen.py --model ${model} \
 --start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
 --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --mbpp_path "mbppplus.json" --greedy_decode
 ) &
 if (($index % $gpu_num == 0)); then wait; fi
done

Code Generation (w/ vllm accelerate)

model="WizardLM/WizardCoder-33B-V1.1"
temp=0.0
max_len=2048
pred_num=1
num_seqs_per_iter=1

output_path=preds/MBPP_T${temp}_N${pred_num}_WizardCoder-33B-V1.1_Greedy_Decode_vllm

mkdir -p ${output_path}
echo 'Output path: '$output_path
echo 'Model to eval: '$model

CUDA_VISIBLE_DEVICES=0,1,2,3 python mbppplus_gen_vllm.py --model ${model} \
 --start_index ${start_index} --end_index ${end_index} --temperature ${temp} \
 --num_seqs_per_iter ${num_seqs_per_iter} --N ${pred_num} --max_len ${max_len} --output_path ${output_path} --mbpp_path "mbppplus.json" --num_gpus 4

• Step 2: Get the score

Install Eval-Plus benchmark.

git clone https://github.com/evalplus/evalplus.git
cd evalplus
export PYTHONPATH=$PYTHONPATH:$(pwd)
pip install -r requirements.txt

Get HumanEval and HumanEval-Plus scores.

output_path=preds/MBPP_T0.0_N1_WizardCoder-33B-V1.1_Greedy_Decode

echo 'Output path: '$output_path
python mbppplus_process_preds.py --path ${output_path} --out_path ${output_path}.jsonl --add_prompt

evalplus.evaluate --dataset mbpp --samples ${output_path}.jsonl

Citation

Please cite the repo if you use the data, method or code in this repo.

@article{luo2023wizardcoder,
 title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct},
 author={Luo, Ziyang and Xu, Can and Zhao, Pu and Sun, Qingfeng and Geng, Xiubo and Hu, Wenxiang and Tao, Chongyang and Ma, Jing and Lin, Qingwei and Jiang, Daxin},
 journal={arXiv preprint arXiv:2306.08568},
 year={2023}
}