OpenCompass/docs/en/get_started.md

Installation

1. Use the following commands to set up the OpenCompass environment:

conda create --name opencompass python=3.10 pytorch torchvision pytorch-cuda -c nvidia -c pytorch -y
conda activate opencompass

If you want to customize the PyTorch version or related CUDA version, please refer to the official documentation to set up the PyTorch environment. Note that OpenCompass requires pytorch>=1.13.

2. Install OpenCompass:

git clone https://github.com/opencompass/opencompass
cd opencompass
pip install -e .

3. Install humaneval (Optional)

If you want to perform evaluations on the humaneval dataset, follow these steps.

git clone https://github.com/openai/human-eval.git
cd human-eval
pip install -r requirements.txt
pip install -e .
cd ..

Please read the comments in human_eval/execution.py lines 48-57 to understand the potential risks of executing the model generation code. If you accept these risks, uncomment line 58 to enable code execution evaluation.

Quick Start

In this section, we will use the example of testing LLaMA-7B on SIQA and PIQA to familiarize you with some basic features of OpenCompass. Before running, make sure you have installed OpenCompass and have GPU computing resources that meet the minimum requirements for LLaMA-7B.

Prepare the Dataset

To start a simple evaluation task using OpenCompass, you generally need to follow three steps:

1. Prepare dataset configurations - configs/datasets provides over 50 datasets supported by OpenCompass.
2. Prepare model configurations - The configs/models contains sample configuration files for already supported large models including those based on HuggingFace and similar APIs like ChatGPT.
3. Use the 'run' script to launch - Supported commands include running locally or on Slurm, testing multiple datasets and models at once.

In this example, we will demonstrate how to test the performance of pre-trained base models from LLaMA-7B on two benchmark tasks, SIQA and PIQA. Before proceeding, ensure that you have installed OpenCompass and have access to sufficient computing resources with GPU support that meet the minimum requirements for LLaMA-7B.

To initiate the evaluation task on your local machine, use the following command:

python run.py configs/eval_llama_7b.py --debug

Here's a detailed step-by-step explanation of this case study:

Step by step

prepare datasets

The SiQA and PiQA benchmarks can be automatically downloaded through their respective links here and here, so no manual downloading is required here. However, some other datasets may require manual downloads. Please refer to the documentation Prepare Datasets for more information.

Create a '.py' configuration file and add the following content:

from mmengine.config import read_base

with read_base():
    # Read the required dataset configurations directly from the preset dataset configurations
    from .datasets.piqa.piqa_ppl import piqa_datasets
    from .datasets.siqa.siqa_gen import siqa_datasets

# Concatenate the datasets to be evaluated into the datasets field
datasets = [*piqa_datasets, *siqa_datasets]

prepare models

The pretrained model 'huggyllama/llama-7b' from HuggingFace supports automatic downloading. Add the following line to your configuration file:

# Evaluate models supported by HuggingFace's `AutoModelForCausalLM` using `HuggingFaceCausalLM`
from opencompass.models import HuggingFaceCausalLM

llama_7b = dict(
        type=HuggingFaceCausalLM,
        # Initialization parameters for `HuggingFaceCausalLM`
        path='huggyllama/llama-7b',
        tokenizer_path='huggyllama/llama-7b',
        tokenizer_kwargs=dict(padding_side='left', truncation_side='left'),
        max_seq_len=2048,
        # Common parameters for all models, not specific to HuggingFaceCausalLM's initialization parameters
        abbr='llama-7b',            # Model abbreviation for result display
        max_out_len=100,            # Maximum number of generated tokens
        batch_size=16,
        run_cfg=dict(num_gpus=1),   # Run configuration for specifying resource requirements
    )

models = [llama_7b]

Lauch Evalution

First, we can start the task in debug mode to check for any exceptions in model loading, dataset reading, or incorrect cache usage.

python run.py configs/llama.py -w outputs/llama --debug

However, in --debug mode, tasks are executed sequentially. After confirming that everything is correct, you can disable the --debug mode to fully utilize multiple GPUs.

python run.py configs/llama.py -w outputs/llama

Here are some parameters related to evaluation that can help you configure more efficient inference tasks based on your environment:

• -w outputs/llama: Directory to save evaluation logs and results.
• -r: Restart the previous (interrupted) evaluation.
• --mode all: Specify a specific stage of the task.
  • all: Perform a complete evaluation, including inference and evaluation.
  • infer: Perform inference on each dataset.
  • eval: Perform evaluation based on the inference results.
  • viz: Display evaluation results only.
• --max-partition-size 2000: Dataset partition size. Some datasets may be large, and using this parameter can split them into multiple sub-tasks to efficiently utilize resources. However, if the partition is too fine, the overall speed may be slower due to longer model loading times.
• --max-num-workers 32: Maximum number of parallel tasks. In distributed environments such as Slurm, this parameter specifies the maximum number of submitted tasks. In a local environment, it specifies the maximum number of tasks executed in parallel. Note that the actual number of parallel tasks depends on the available GPU resources and may not be equal to this number.

If you are not performing the evaluation on your local machine but using a Slurm cluster, you can specify the following parameters:

• --slurm: Submit tasks using Slurm on the cluster.
• --partition my_part: Slurm cluster partition.
• --retry 2: Number of retries for failed tasks.

Obtaining Evaluation Results

After the evaluation is complete, the evaluation results table will be printed as follows:

dataset    version    metric    mode      llama-7b
---------  ---------  --------  ------  ----------
piqa       1cf9f0     accuracy  ppl          77.75
siqa       e78df3     accuracy  gen          36.08

All run outputs will default to outputs/default/ directory with following structure:

outputs/default/
├── 20200220_120000
├── ...
├── 20230220_183030
│   ├── configs
│   ├── logs
│   │   ├── eval
│   │   └── infer
│   ├── predictions
│   │   └── MODEL1
│   └── results
│       └── MODEL1

Inside each timestamp folder there would be below items:

• configs folder, used for storing configuration files corresponding to this output dir using current time stamp;
• logs folder, used for storing inference and evaluation log files of different models;
• predictions folder, used for storing inference json result file(s), grouped by model;
• results folder, used for storing evaluation json result file(s), grouped by model.