Please cite our paper, [SGLang: Efficient Execution of Structured Language Model Programs](https://arxiv.org/abs/2312.07104), if you find the project useful.
- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ).
- **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
- **Extensive Model Support**: Supports a wide range of generative models (Llama 3, Gemma 2, Mistral, QWen, DeepSeek, LLaVA, etc.) and embedding models (e5-mistral), with easy extensibility for integrating new models.
- **Active Community**: SGLang is open-source and backed by an active community with industry adoption, welcoming contributions to improve LLM and VLM serving.
- **Active Community**: SGLang is open-source and backed by an active community with industry adoption.
To support a new model in SGLang, you only need to add a single file under [SGLang Models Directory](https://github.com/sgl-project/sglang/tree/main/python/sglang/srt/models). You can learn from existing model implementations and create new files for the new models. Most models are based on the transformer architecture, making them very similar.
To support a new model in SGLang, you only need to add a single file under [SGLang Models Directory](https://github.com/sgl-project/sglang/tree/main/python/sglang/srt/models).
You can learn from existing model implementations and create new files for the new models.
For most models, you should be able to find a similar model to start with (e.g., starting from Llama).
Another valuable resource is the [vLLM Models Directory](https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models). vLLM has extensive coverage of models, and SGLang has reused vLLM for most parts of the model implementations. This similarity makes it easy to port many models from vLLM to SGLang.
## Test the correctness
To port a model from vLLM to SGLang, you can compare these two files [SGLang LLaMA Implementation](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/llama.py) and [vLLM LLaMA Implementation](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llama.py). This comparison will help you understand how to convert a model implementation from vLLM to SGLang. The major difference is the replacement of PagedAttention with RadixAttention. The other parts are almost identical. Specifically,
### Interactive debugging
For interactive debugging, you can compare the outputs of huggingface/transformers and SGLang.
The following two commands should give the same text output and very similar prefill logits.
- Get the reference output by `python3 scripts/playground/reference_hf.py --model [new model]`
- Get the SGLang output by `python3 -m sglang.bench_latency --correct --model [new model]`
### Add the model to the test suite
To make sure the new model is well maintained in the future, it is better to add it to the test suite.
You can add it to the `ALL_OTHER_MODELS` list in the [test_generation_models.py](https://github.com/sgl-project/sglang/blob/main/test/srt/models/test_generation_models.py) and run the following command to test it.
Another valuable resource is the [vLLM Models Directory](https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models). vLLM has extensive coverage of models, and SGLang reuses vLLM's interface and some layers to implement the models. This similarity makes it easy to port many models from vLLM to SGLang.
To port a model from vLLM to SGLang, you can compare these two files [SGLang Llama Implementation](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/llama.py) and [vLLM Llama Implementation](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llama.py). This comparison will help you understand how to convert a model implementation from vLLM to SGLang. The major difference is the replacement of Attention with RadixAttention. The other parts are almost identical. Specifically,
- Replace vllm's `Attention` with `RadixAttention`. Note that you need to pass `layer_id` all the way to `RadixAttention`.
- Replace vllm's `LogitsProcessor` with SGLang's `LogitsProcessor`.
- Replace other vLLM layers with SGLang layers (e.g., `RMSNorm`, `SiluAndMul`).
- Remove `Sample`.
- Change `forward()` functions, and add `input_metadata`.
- Add `EntryClass` at the end.
- Test correctness by comparing the final logits and outputs of the two following commands:
