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Installation
- 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.
- Install OpenCompass:
git clone https://github.com/InternLM/opencompass.git
cd opencompass
pip install -e .
- Install humaneval (Optional)
If you want to evaluate your models coding ability on the humaneval dataset, execute this step otherwise skip it.
click to show the details
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.
Dataset Preparation
The datasets supported by OpenCompass mainly include two parts:
- Huggingface datasets: The Huggingface Datasets provide a large number of datasets, which will automatically download when running with this option.
- Custom dataset: OpenCompass also provides some Chinese custom self-built datasets. Please run the following command to manually download and extract them.
Run the following commands to download and place the datasets in the ${OpenCompass}/data directory can complete dataset preparation.
# Run in the OpenCompass directory
wget https://github.com/InternLM/opencompass/releases/download/0.1.0/OpenCompassData.zip
unzip OpenCompassData.zip
OpenCompass has supported most of the datasets commonly used for performance comparison, please refer to configs/dataset for the specific list of supported datasets.
Quick Start
The evaluation of OpenCompass relies on configuration files which must contain fields datasets and models.
The configurations specify the models and datasets to evaluate using "run.py".
We will demonstrate some basic features of OpenCompass through evaluating pretrained models OPT-125M and OPT-350M on both SIQA and Winograd benchmark tasks with their config file located at configs/eval_demo.py.
Before running this experiment, please make sure you have installed OpenCompass locally and it should run successfully under one GTX-1660-6G GPU. For larger parameterized models like Llama-7B, refer to other examples provided in the configs directory.
Since OpenCompass launches evaluation processes in parallel by default, we can start the evaluation for the first run and check if there is any prblem. In debugging mode, the tasks will be executed sequentially and the status will be printed in real time.
python run.py configs/eval_demo.py -w outputs/demo --debug
If everything is fine, you should see "Starting inference process" on screen:
[2023-07-12 18:23:55,076] [opencompass.openicl.icl_inferencer.icl_gen_inferencer] [INFO] Starting inference process...
Then you can press ctrl+c to interrupt the program, and then run the following command to start the parallel evaluation:
python run.py configs/eval_demo.py -w outputs/demo
Now let's go over the configuration file and the launch options used in this case.
Explanations
Dataset list - datasets
Below is the configuration snippet related to datasets in configs/eval_demo.py:
from mmengine.config import read_base # Use mmengine.read_base() to load base configs
with read_base():
# Read the required dataset configurations directly from the preset dataset configurations
from .datasets.winograd.winograd_ppl import winograd_datasets # Load Winograd's configuration, which uses perplexity-based inference
from .datasets.siqa.siqa_gen import siqa_datasets # Load SIQA's configuration, which uses generation-based inference
datasets = [*siqa_datasets, *winograd_datasets] # Concatenate the datasets to be evaluated into the datasets field
Various dataset configurations are available in configs/datasets.
Some datasets have two types of configuration files within their folders named ppl and gen, representing different evaluation methods. Specifically, ppl represents discriminative evaluation, while gen stands for generative evaluation.
configs/datasets/collections contains various collections of datasets for comprehensive evaluation purposes.
You can find more information from Dataset Preparation.
Model list - models
OpenCompass supports directly specifying the list of models to be tested in the configuration. For HuggingFace models, users usually do not need to modify the code. The following is the relevant configuration snippet:
# Evaluate models supported by HuggingFace's `AutoModelForCausalLM` using `HuggingFaceCausalLM`
from opencompass.models import HuggingFaceCausalLM
# OPT-350M
opt350m = dict(
type=HuggingFaceCausalLM,
# Initialization parameters for `HuggingFaceCausalLM`
path='facebook/opt-350m',
tokenizer_path='facebook/opt-350m',
tokenizer_kwargs=dict(
padding_side='left',
truncation_side='left',
proxies=None,
trust_remote_code=True),
model_kwargs=dict(device_map='auto'),
# Below are common parameters for all models, not specific to HuggingFaceCausalLM
abbr='opt350m', # Model abbreviation for result display
max_seq_len=2048, # The maximum length of the entire sequence
max_out_len=100, # Maximum number of generated tokens
batch_size=64, # batchsize
run_cfg=dict(num_gpus=1), # Run configuration for specifying resource requirements
)
# OPT-125M
opt125m = dict(
type=HuggingFaceCausalLM,
# Initialization parameters for `HuggingFaceCausalLM`
path='facebook/opt-125m',
tokenizer_path='facebook/opt-125m',
tokenizer_kwargs=dict(
padding_side='left',
truncation_side='left',
proxies=None,
trust_remote_code=True),
model_kwargs=dict(device_map='auto'),
# Below are common parameters for all models, not specific to HuggingFaceCausalLM
abbr='opt125m', # Model abbreviation for result display
max_seq_len=2048, # The maximum length of the entire sequence
max_out_len=100, # Maximum number of generated tokens
batch_size=128, # batchsize
run_cfg=dict(num_gpus=1), # Run configuration for specifying resource requirements
)
models = [opt350m, opt125m]
The pretrained models 'facebook/opt-350m' and 'facebook/opt-125m' will be automatically downloaded from HuggingFace during the first run.
More information about model configuration can be found in Prepare Models.
Launch Evaluation
When the config file is ready, 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/eval_demo.py -w outputs/demo --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/eval_demo.py -w outputs/demo
Here are some parameters related to evaluation that can help you configure more efficient inference tasks based on your environment:
-w outputs/demo: 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(-p) my_part: Slurm cluster partition.--retry 2: Number of retries for failed tasks.
The entry also supports submitting tasks to Alibaba Deep Learning Center (DLC), and more customized evaluation strategies. Please refer to [Launching an Evaluation Task](./user_guides/experimentation.md#launching-an-evaluation-task) for details.
Obtaining Evaluation Results
After the evaluation is complete, the evaluation results table will be printed as follows:
dataset version metric mode opt350m opt125m
--------- --------- -------- ------ --------- ---------
siqa e78df3 accuracy gen 21.55 12.44
winograd b6c7ed accuracy ppl 51.23 49.82
All run outputs will be directed to outputs/demo/ directory with following structure:
outputs/default/
├── 20200220_120000
├── 20230220_183030 # one experiment pre folder
│ ├── configs # Dumped config files for record. Multiple configs may be kept if different experiments have been re-run on the same experiment folder
│ ├── logs # log files for both inference and evaluation stages
│ │ ├── eval
│ │ └── infer
│ ├── predictions # Prediction results for each task
│ ├── results # Evaluation results for each task
│ └── summary # Summarized evaluation results for a single experiment
├── ...
Additional Tutorials
To learn more about using OpenCompass, explore the following tutorials: