# Code Evaluation Docker Tutorial

To complete the LLM code capability evaluation, we need to build a separate evaluation environment to avoid executing erroneous code in the development environment, which would inevitably cause losses. The code evaluation service currently used by OpenCompass can refer to the [code-evaluator](https://github.com/open-compass/code-evaluator) project. The following will introduce evaluation tutorials around the code evaluation service.

1. humaneval-x

This is a multi-programming language dataset [humaneval-x](https://huggingface.co/datasets/THUDM/humaneval-x).
You can download the dataset from this [download link](https://github.com/THUDM/CodeGeeX2/tree/main/benchmark/humanevalx). Please download the language file (××.jsonl.gz) that needs to be evaluated and place it in the `./data/humanevalx` folder.

The currently supported languages are `python`, `cpp`, `go`, `java`, `js`.

2. DS1000

This is a Python multi-algorithm library dataset [ds1000](https://github.com/xlang-ai/DS-1000).
You can download the dataset from this [download link](https://github.com/xlang-ai/DS-1000/blob/main/ds1000_data.zip).

The currently supported algorithm libraries are `Pandas`, `Numpy`, `Tensorflow`, `Scipy`, `Sklearn`, `Pytorch`, `Matplotlib`.

## Launching the Code Evaluation Service

1. Ensure you have installed Docker, please refer to [Docker installation document](https://docs.docker.com/engine/install/).
2. Pull the source code of the code evaluation service project and build the Docker image.

Choose the dockerfile corresponding to the dataset you need, and replace `humanevalx` or `ds1000` in the command below.

```shell
git clone https://github.com/open-compass/code-evaluator.git
sudo docker build -t code-eval-{your-dataset}:latest -f docker/{your-dataset}/Dockerfile .
```

3. Create a container with the following commands:

```shell
# Log output format
sudo docker run -it -p 5000:5000 code-eval-{your-dataset}:latest python server.py

# Run the program in the background
# sudo docker run -itd -p 5000:5000 code-eval-{your-dataset}:latest python server.py

# Using different ports
# sudo docker run -itd -p 5001:5001 code-eval-{your-dataset}:latest python server.py --port 5001
```

4. To ensure you have access to the service, use the following command to check the inference environment and evaluation service connection status. (If both inferences and code evaluations run on the same host, skip this step.)

```shell
ping your_service_ip_address
telnet your_service_ip_address your_service_port
```

## Local Code Evaluation

When the model inference and code evaluation services are running on the same host or within the same local area network, direct code reasoning and evaluation can be performed. **Note: DS1000 is currently not supported, please proceed with remote evaluation.**

### Configuration File

We provide [the configuration file](https://github.com/open-compass/opencompass/blob/main/configs/eval_codegeex2.py) of using `humanevalx` for evaluation on `codegeex2` as reference.

The dataset and related post-processing configurations files can be found at this [link](https://github.com/open-compass/opencompass/tree/main/configs/datasets/humanevalx) with attention paid to the `evaluator` field in the humanevalx_eval_cfg_dict.

```python
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import HumanevalXDataset, HumanevalXEvaluator

humanevalx_reader_cfg = dict(
    input_columns=['prompt'], output_column='task_id', train_split='test')

humanevalx_infer_cfg = dict(
    prompt_template=dict(
        type=PromptTemplate,
        template='{prompt}'),
    retriever=dict(type=ZeroRetriever),
    inferencer=dict(type=GenInferencer, max_out_len=1024))

humanevalx_eval_cfg_dict = {
    lang : dict(
            evaluator=dict(
                type=HumanevalXEvaluator,
                language=lang,
                ip_address="localhost",    # replace to your code_eval_server ip_address, port
                port=5000),               # refer to https://github.com/open-compass/code-evaluator to launch a server
            pred_role='BOT')
    for lang in ['python', 'cpp', 'go', 'java', 'js']   # do not support rust now
}

humanevalx_datasets = [
    dict(
        type=HumanevalXDataset,
        abbr=f'humanevalx-{lang}',
        language=lang,
        path='./data/humanevalx',
        reader_cfg=humanevalx_reader_cfg,
        infer_cfg=humanevalx_infer_cfg,
        eval_cfg=humanevalx_eval_cfg_dict[lang])
    for lang in ['python', 'cpp', 'go', 'java', 'js']
]
```

### Task Launch

Refer to the [Quick Start](../get_started.html)

## Remote Code Evaluation

Model inference and code evaluation services located in different machines which cannot be accessed directly require prior model inference before collecting the code evaluation results. The configuration file and inference process can be reused from the previous tutorial.

### Collect Inference Results(Only for Humanevalx)

In OpenCompass's tools folder, there is a script called `collect_code_preds.py` provided to process and collect the inference results after providing the task launch configuration file during startup along with specifying the working directory used corresponding to the task.
It is the same with `-r` option in `run.py`. More details can be referred through the [documentation](https://opencompass.readthedocs.io/en/latest/get_started.html#launch-evaluation).

```shell
python tools/collect_code_preds.py [config] [-r latest]
```

The collected results will be organized as following under the `-r` folder:

```
workdir/humanevalx
├── codegeex2-6b
│   ├── humanevalx_cpp.json
│   ├── humanevalx_go.json
│   ├── humanevalx_java.json
│   ├── humanevalx_js.json
│   └── humanevalx_python.json
├── CodeLlama-13b
│   ├── ...
├── CodeLlama-13b-Instruct
│   ├── ...
├── CodeLlama-13b-Python
│   ├── ...
├── ...
```

For DS1000, you just need to obtain the corresponding prediction file generated by `opencompass`.

### Code Evaluation

Make sure your code evaluation service is started, and use `curl` to request:

#### The following only supports Humanevalx

```shell
curl -X POST -F 'file=@{result_absolute_path}' -F 'dataset={dataset/language}' {your_service_ip_address}:{your_service_port}/evaluate
```

For example:

```shell
curl -X POST -F 'file=@./examples/humanevalx/python.json' -F 'dataset=humanevalx/python' localhost:5000/evaluate
```

The we have:

```
"{\"pass@1\": 37.19512195121951%}"
```

Additionally, we offer an extra option named `with_prompt`(Defaults to `True`), since some models(like `WizardCoder`) generate complete codes without requiring the form of concatenating prompt and prediction. You may refer to the following commands for evaluation.

```shell
curl -X POST -F 'file=@./examples/humanevalx/python.json' -F 'dataset=humanevalx/python' -H 'with-prompt: False' localhost:5000/evaluate
```

#### The following only supports DS1000

Make sure the code evaluation service is started, then use `curl` to submit a request:

```shell
curl -X POST -F 'file=@./internlm-chat-7b-hf-v11/ds1000_Numpy.json' localhost:5000/evaluate
```

DS1000 supports additional debug parameters. Be aware that a large amount of log will be generated when it is turned on:

- `full`: Additional print out of the original prediction for each error sample, post-processing prediction, running program, and final error.
- `half`: Additional print out of the running program and final error for each error sample.
- `error`: Additional print out of the final error for each error sample.

```shell
curl -X POST -F 'file=@./internlm-chat-7b-hf-v11/ds1000_Numpy.json' -F 'debug=error' localhost:5000/evaluate
```

You can also modify the `num_workers` in the same way to control the degree of parallelism.

## Advanced Tutorial

Besides evaluating the supported HUMANEVAList data set, users might also need:

### Support New Dataset

Please refer to the [tutorial on supporting new datasets](./new_dataset.md).

### Modify Post-Processing

1. For local evaluation, follow the post-processing section in the tutorial on supporting new datasets to modify the post-processing method.
2. For remote evaluation, please modify the post-processing part in the tool's `collect_code_preds.py`.
3. Some parts of post-processing could also be modified in the code evaluation service, more information will be available in the next section.

### Debugging Code Evaluation Service

When supporting new datasets or modifying post-processors, it is possible that modifications need to be made to the original code evaluation service. Please make changes based on the following steps:

1. Remove the installation of the `code-evaluator` in `Dockerfile`, mount the `code-evaluator` when starting the container instead:

```shell
sudo docker run -it -p 5000:5000 -v /local/path/of/code-evaluator:/workspace/code-evaluator code-eval:latest bash
```

2. Install and start the code evaluation service locally. At this point, any necessary modifications can be made to the local copy of the `code-evaluator`.

```shell
cd code-evaluator && pip install -r requirements.txt
python server.py
```
