146 lines
5.2 KiB
Markdown
146 lines
5.2 KiB
Markdown
# SGL Kernel
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[Kernel Library](https://github.com/sgl-project/sglang/tree/main/sgl-kernel) for SGLang
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[](https://pypi.org/project/sgl-kernel)
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## Installation
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For CUDA 11.8:
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```bash
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pip3 install sgl-kernel -i https://docs.sglang.ai/whl/cu118
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```
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For CUDA 12.1 or CUDA 12.4:
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```bash
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pip3 install sgl-kernel
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```
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# Developer Guide
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## Development Environment Setup
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Use Docker to set up the development environment. See [Docker setup guide](https://github.com/sgl-project/sglang/blob/main/docs/developer/development_guide_using_docker.md#setup-docker-container).
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Create and enter development container:
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```bash
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docker run -itd --shm-size 32g --gpus all -v $HOME/.cache:/root/.cache --ipc=host --name sglang_zhyncs lmsysorg/sglang:dev /bin/zsh
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docker exec -it sglang_zhyncs /bin/zsh
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```
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## Project Structure
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### Dependencies
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Third-party libraries:
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- [CCCL](https://github.com/NVIDIA/cccl)
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- [CUTLASS](https://github.com/NVIDIA/cutlass)
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- [FlashInfer](https://github.com/flashinfer-ai/flashinfer)
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- [DeepGEMM](https://github.com/deepseek-ai/DeepGEMM)
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### Kernel Development
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Steps to add a new kernel:
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1. Implement the kernel in [csrc](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc)
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2. Expose the interface in [include/sgl_kernel_ops.h](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/include/sgl_kernel_ops.h)
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3. Create torch extension in [csrc/torch_extension.cc](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/csrc/torch_extension.cc)
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4. Update [CMakeLists.txt](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/CMakeLists.txt) to include new CUDA source
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5. Expose Python interface in [python](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/python/sgl_kernel)
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### Development Tips
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1. When implementing kernels in [csrc](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc), only define pure CUDA files and C++ interfaces. If you need to use `Torch::tensor`, use `<torch/all.h>` instead of `<torch/extension.h>`. Using `<torch/extension.h>` will cause compilation errors when using SABI.
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2. When creating torch extensions, simply add the function definition with `m.def`:
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```cpp
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m.def("register_graph_buffers", register_graph_buffers);
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```
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3. When exposing Python interfaces, avoid using kwargs in C++ interface kernels.
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**Avoid this:**
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```cpp
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torch.ops.sgl_kernel.apply_rope_pos_ids_cos_sin_cache.default(
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q=query.view(query.shape[0], -1, head_size),
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k=key.view(key.shape[0], -1, head_size),
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q_rope=query.view(query.shape[0], -1, head_size),
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k_rope=key.view(key.shape[0], -1, head_size),
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cos_sin_cache=cos_sin_cache,
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pos_ids=positions.long(),
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interleave=(not is_neox),
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cuda_stream=get_cuda_stream(),
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)
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```
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**Use this instead:**
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```cpp
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torch.ops.sgl_kernel.apply_rope_pos_ids_cos_sin_cache.default(
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query.view(query.shape[0], -1, head_size),
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key.view(key.shape[0], -1, head_size),
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query.view(query.shape[0], -1, head_size),
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key.view(key.shape[0], -1, head_size),
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cos_sin_cache,
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positions.long(),
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(not is_neox),
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get_cuda_stream(),
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)
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```
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### Integrating Third-Party Libraries with Data Type Conversion
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When integrating new third-party libraries like flash-attention, you may encounter data type compatibility issues between the C++ interface and PyTorch bindings. For example, the third-party code might use `float` or `int` types, while PyTorch requires `double` and `int64_t`.
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To address this issue, we provide the `make_pytorch_shim` function in [sgl_kernel_torch_shim](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/include/sgl_kernel_torch_shim.h) that handles data type conversions automatically.
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When you need to support new data type conversions, you can easily add conversion functions like this:
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```cpp
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// Map `int` -> `int64_t`
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template <>
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struct pytorch_library_compatible_type<int> {
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using type = int64_t;
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static int convert_from_type(int64_t arg) {
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TORCH_CHECK(arg <= std::numeric_limits<int>::max(), "int64_t value is too large to be converted to int");
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TORCH_CHECK(arg >= std::numeric_limits<int>::min(), "int64_t value is too small to be converted to int");
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return arg;
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}
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};
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```
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To use this with your library functions, simply wrap them with make_pytorch_shim:
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```cpp
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/*
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* From flash-attention
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*/
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m.def("fwd", make_pytorch_shim(mha_fwd));
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```
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### Build & Install
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Development build:
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```bash
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make build
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```
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Note:
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The `sgl-kernel` is rapidly evolving. If you experience a compilation failure, try using `make rebuild`.
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### Testing & Benchmarking
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1. Add pytest tests in [tests/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/tests)
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2. Add benchmarks using [triton benchmark](https://triton-lang.org/main/python-api/generated/triton.testing.Benchmark.html) in [benchmark/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/benchmark)
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3. Run test suite
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### Release new version
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Update version in [pyproject.toml](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/pyproject.toml) and [version.py](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/python/sgl_kernel/version.py)
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