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Reproduce the experiments

Important

General requirements

Before you start, make sure you have cloned the repository and you are in the root directory of the project. Make sure you installed the required packages with pip install -e .. Different package versions may impact the reproducibility of the results.

We obtained the results with the subsequent hardware and environment:

  • One NVIDIA A100 80G GPU
  • Python 3.10.0

In case you face issues, we provide the raw outputs we generated in the evalplus_results directory.

Reproduce HumanEval(+) and MBPP(+)

We pack multiple problems into one batch to speed up the inference. A different batch size may lead to slightly worse/better results due to the floating point round off resulted from the underlying cuBLAS optimization.

Make sure you set CUDA_VISIBLE_DEVICES to the GPU you want to use and cded to the root directory of the repo. We assume you use device 0 in the following commands.

HumanEval(+)

MODEL_KEY=bigcode/starcoder2-15b-instruct-v0.1
MODEL=bigcode/starcoder2-15b-instruct-v0.1
DATASET=humaneval
SAVE_PATH=evalplus-$(basename $MODEL)-$DATASET.jsonl
CUDA_VISIBLE_DEVICES=0

CUDA_VISIBLE_DEVICES=0 python -m evaluation.text2code \
  --model_key $MODEL_KEY \
  --model_name_or_path $MODEL \
  --save_path $SAVE_PATH \
  --dataset $DATASET \
  --temperature 0.0 \
  --top_p 1.0 \
  --max_new_tokens 512 \
  --n_problems_per_batch 16 \
  --n_samples_per_problem 1 \
  --n_batches 1

python -m evalplus.evaluate --dataset $DATASET --samples $SAVE_PATH
# humaneval (base tests)
# pass@1: 0.726
# humaneval+ (base + extra tests)
# pass@1: 0.634

MBPP(+)

MODEL_KEY=bigcode/starcoder2-15b-instruct-v0.1
MODEL=bigcode/starcoder2-15b-instruct-v0.1
DATASET=mbpp
SAVE_PATH=evalplus-$(basename $MODEL)-$DATASET.jsonl

CUDA_VISIBLE_DEVICES=0 python -m evaluation.text2code \
  --model_key $MODEL_KEY \
  --model_name_or_path $MODEL \
  --save_path $SAVE_PATH \
  --dataset $DATASET \
  --temperature 0.0 \
  --top_p 1.0 \
  --max_new_tokens 512 \
  --n_problems_per_batch 16 \
  --n_samples_per_problem 1 \
  --n_batches 1

python -m evalplus.evaluate --dataset $DATASET --samples $SAVE_PATH
# mbpp (base tests)
# pass@1: 0.642
# mbpp+ (base + extra tests)
# pass@1: 0.526