amd64/cu128/: sgl-kernel-0.1.1 metadata and description
Kernel Library for SGLang
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description_content_type | text/markdown |
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requires_python | >=3.9 |
Because this project isn't in the mirror_whitelist
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no releases from root/pypi are included.
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sgl_kernel-0.1.1-cp39-abi3-linux_x86_64.whl
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SGL Kernel
Kernel Library for SGLang
Installation
For CUDA 11.8:
pip3 install sgl-kernel -i https://docs.sglang.ai/whl/cu118
For CUDA 12.1 or CUDA 12.4:
pip3 install sgl-kernel
Build from source
Development build:
make build
Note:
The sgl-kernel
is rapidly evolving. If you experience a compilation failure, try using make rebuild
.
Build with ccache
# or `yum install -y ccache`.
apt-get install -y ccache
# Building with ccache is enabled when ccache is installed and CCACHE_DIR is set.
export CCACHE_DIR=/path/to/your/ccache/dir
export CCACHE_BACKEND=""
export CCACHE_KEEP_LOCAL_STORAGE="TRUE"
unset CCACHE_READONLY
python -m uv build --wheel -Cbuild-dir=build --color=always .
Configuring CMake Build Options
Cmake options can be configuring by adding -Ccmake.define.<option>=<value>
to the uv build
flags.
For example, to enable building FP4 kernels, use:
python -m uv build --wheel -Cbuild-dir=build -Ccmake.define.SGL_KERNEL_ENABLE_FP4=1 --color=always .
See CMakeLists.txt for more options.
Parallel Build
We highly recommend you build sgl-kernel with Ninja. Ninja can automatically build sgl-kernel in parallel.
And if you build the sgl-kernel with cmake, you need to add CMAKE_BUILD_PARALLEL_LEVEL
for parallel build like:
CMAKE_BUILD_PARALLEL_LEVEL=$(nproc) python -m uv build --wheel -Cbuild-dir=build --color=always .
Developer Guide
Development Environment Setup
Use Docker to set up the development environment. See Docker setup guide.
Create and enter development container:
docker run -itd --shm-size 32g --gpus all -v $HOME/.cache:/root/.cache --ipc=host --name sglang_zhyncs lmsysorg/sglang:dev /bin/zsh
docker exec -it sglang_zhyncs /bin/zsh
Project Structure
Dependencies
Third-party libraries:
FlashAttention FYI
FA3 can fail without a enough shared memory for a some shapes, such as higher hidden_dim or some special cases. Right now, fa3 is supported for sm80/sm87 and sm86/sm89.
The main different Between sm80/sm87 and sm86/sm89 is the shared memory size. you can follow the link below for more information https://docs.nvidia.com/cuda/cuda-c-programming-guide/#shared-memory-8-x.
And for sgl-kernel right now, we can build fa3 on sm80/sm86/sm89/sm90a. Thats mean if you use A100(tested)/A*0/L20(tested)/L40/L40s/3090(tested) you can use fa3.
Kernel Development
Steps to add a new kernel:
- Implement the kernel in csrc
- Expose the interface in include/sgl_kernel_ops.h
- Create torch extension in csrc/common_extension.cc
- Update CMakeLists.txt to include new CUDA source
- Expose Python interface in python
Development Tips
-
When implementing kernels in 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. -
When creating torch extensions, add the function definition with
m.def
, and device binding withm.impl
:
-
Using torch.compile need
m.def
with schema, it helps auto capture the custom kernel. Reference: How to add FakeTensor -
How to write schema: Schema reference
// We need def with schema here for torch.compile m.def( "bmm_fp8(Tensor A, Tensor B, Tensor! D, Tensor A_scale, Tensor B_scale, Tensor workspace_buffer, int " "cublas_handle, int cuda_stream) -> ()"); m.impl("bmm_fp8", torch::kCUDA, &bmm_fp8);
-
When exposing Python interfaces, avoid using kwargs in C++ interface kernels.
Avoid this:
torch.ops.sgl_kernel.apply_rope_pos_ids_cos_sin_cache.default( q=query.view(query.shape[0], -1, head_size), k=key.view(key.shape[0], -1, head_size), q_rope=query.view(query.shape[0], -1, head_size), k_rope=key.view(key.shape[0], -1, head_size), cos_sin_cache=cos_sin_cache, pos_ids=positions.long(), interleave=(not is_neox), cuda_stream=get_cuda_stream(), )
Use this instead:
torch.ops.sgl_kernel.apply_rope_pos_ids_cos_sin_cache.default( query.view(query.shape[0], -1, head_size), key.view(key.shape[0], -1, head_size), query.view(query.shape[0], -1, head_size), key.view(key.shape[0], -1, head_size), cos_sin_cache, positions.long(), (not is_neox), get_cuda_stream(), )
Integrating Third-Party Libraries with Data Type Conversion
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
.
The reason we need
double
andint64_t
in torch binding is that TORCH_LIBRARY handles thePython-to-C++
conversion process. Python'sfloat
data type actually corresponds todouble
in C++, while Python'sint
corresponds toint64_t
in C++.
To address this issue, we provide the make_pytorch_shim
function in sgl_kernel_torch_shim that handles data type conversions automatically.
When you need to support new data type conversions, you can easily add conversion functions like this:
// Map `int` -> `int64_t`
template <>
struct pytorch_library_compatible_type<int> {
using type = int64_t;
static int convert_from_type(int64_t arg) {
TORCH_CHECK(arg <= std::numeric_limits<int>::max(), "int64_t value is too large to be converted to int");
TORCH_CHECK(arg >= std::numeric_limits<int>::min(), "int64_t value is too small to be converted to int");
return arg;
}
};
To use this with your library functions, simply wrap them with make_pytorch_shim:
/*
* From flash-attention
*/
m.impl("fwd", torch::kCUDA, make_pytorch_shim(&mha_fwd));
Testing & Benchmarking
- Add pytest tests in tests/, if you need to skip some test, please use
@pytest.mark.skipif
@pytest.mark.skipif(
skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
)
- Add benchmarks using triton benchmark in benchmark/
- Run test suite
FAQ
- When encountering this error while compiling using ccache:
ImportError: /usr/local/lib/python3.10/dist-packages/sgl_kernel/common_ops.abi3.so: undefined symbol: _ZN3c108ListType3getERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEENS_4Type24SingletonOrSharedTypePtrIS9_EE
, please modify the last command as follows to resolve it:python3 -m uv build --wheel -Cbuild-dir=build . --color=always --no-build-isolation
.
Release new version
Update version in pyproject.toml and version.py