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adapt to sglang v0.5.2rc1 on dcu

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Metadata-Version: 2.4
Name: sgl-kernel
Version: 0.3.8
Summary: Kernel Library for SGLang
License: Apache License
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# SGL Kernel
[Kernel Library](https://github.com/sgl-project/sglang/tree/main/sgl-kernel) for SGLang
[![PyPI](https://img.shields.io/pypi/v/sgl-kernel)](https://pypi.org/project/sgl-kernel)
## Installation
For CUDA 12.1 and above:
```bash
pip3 install sgl-kernel
```
For CUDA 11.8:
```bash
pip3 install sgl-kernel -i https://docs.sglang.ai/whl/cu118
```
## Build from source
Development build:
```bash
make build
```
Note:
The `sgl-kernel` is rapidly evolving. If you experience a compilation failure, try using `make rebuild`.
### Build with [ccache](https://github.com/ccache/ccache)
```bash
# 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:
```bash
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:
```bash
CMAKE_BUILD_PARALLEL_LEVEL=$(nproc) python -m uv build --wheel -Cbuild-dir=build --color=always .
```
### ⚠️ Compilation Issue with `sgl-kernel` and CUDA 12.6
When compiling `sgl-kernel` with FlashAttention on a Hopper GPU using CUDA 12.6, you may encounter a segmentation fault:
```bash
kernel/build/_deps/repo-flash-attention-src/hopper/instantiations/flash_fwd_hdimall_bf16_paged_softcap_sm90.cu -o CMakeFiles/flash_ops.dir/_deps/repo-flash-attention-src/hopper/instantiations/flash_fwd_hdimall_bf16_paged_softcap_sm90.cu.o
Segmentation fault (core dumped)
```
⚠️ **Note**: To ensure that FlashAttention compiles correctly on Hopper GPU Architecture(sm90), it is strongly [recommended](https://github.com/Dao-AILab/flash-attention/issues/1453) to use:
- nvcc version: 12.6
- ptxas version: 12.8
**1. Check Current Versions**
Before proceeding, verify your current CUDA tool versions:
```bash
nvcc --version
ptxas --version
```
**2. Update ptxas to 12.8 (if needed)**
1. Save the following script to a file (e.g., `update_ptxas.sh`).
```bash
#!/usr/bin/env bash
# Source: https://github.com/Dao-AILab/flash-attention/blob/7ff1b621112ba8b538e2fc6a316f2a6b6f22e518/hopper/setup.py#L404
set -ex
if [ -z "$1" ]; then
echo "Usage: $0 <CUDA_VERSION>"
exit 1
fi
CUDA_VERSION=$1
if awk "BEGIN {exit !("$CUDA_VERSION" >= 12.6 && "$CUDA_VERSION" < 12.8)}"; then
NVCC_ARCHIVE_VERSION="12.8.93"
NVCC_ARCHIVE_NAME="cuda_nvcc-linux-x86_64-${NVCC_ARCHIVE_VERSION}-archive"
NVCC_ARCHIVE_TAR="${NVCC_ARCHIVE_NAME}.tar.xz"
NVCC_ARCHIVE_URL="https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/linux-x86_64/${NVCC_ARCHIVE_TAR}"
wget "$NVCC_ARCHIVE_URL"
tar -xf "$NVCC_ARCHIVE_TAR"
mkdir -p /usr/local/cuda/bin
cp "${NVCC_ARCHIVE_NAME}/bin/ptxas" /usr/local/cuda/bin/
# Clean up temporary files
rm -f "${NVCC_ARCHIVE_TAR}"
rm -rf "${NVCC_ARCHIVE_NAME}"
fi
```
2. Run the script with your CUDA version as the argument, using `sudo`:
```bash
sudo bash update_ptxas.sh 12.6
# Check the version
ptxas --version
```
# Developer Guide
## Development Environment Setup
Use Docker to set up the development environment. See [Docker setup guide](https://github.com/sgl-project/sglang/blob/main/docs/developer_guide/development_guide_using_docker.md#setup-docker-container).
Create and enter development container:
```bash
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:
- [CUTLASS](https://github.com/NVIDIA/cutlass)
- [FlashInfer](https://github.com/flashinfer-ai/flashinfer)
- [DeepGEMM](https://github.com/deepseek-ai/DeepGEMM)
- [FlashAttention](https://github.com/Dao-AILab/flash-attention)
### 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. That means 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:
1. Implement the kernel in [csrc](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc)
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)
3. Create torch extension in [csrc/common_extension.cc](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/csrc/common_extension.cc)
4. Update [CMakeLists.txt](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/CMakeLists.txt) to include new CUDA source
5. Expose Python interface in [python](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/python/sgl_kernel)
### Development Tips
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.
2. When creating torch extensions, add the function definition with `m.def`, and device binding with `m.impl`:
- Using torch.compile need `m.def` with schema, it helps auto capture the custom kernel. Reference: [How to add FakeTensor](https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU/edit?tab=t.0#heading=h.ptttacy8y1u9)
- How to write schema: [Schema reference](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/README.md#func)
```cpp
// 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);
```
3. When exposing Python interfaces, avoid using kwargs in C++ interface kernels.
**Avoid this:**
```cpp
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:**
```cpp
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` and `int64_t` in torch binding is that TORCH_LIBRARY handles the `Python-to-C++` conversion process. Python's `float` data type actually corresponds to `double` in C++, while Python's `int` corresponds to `int64_t` in C++.
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.
When you need to support new data type conversions, you can easily add conversion functions like this:
```cpp
// 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:
```cpp
/*
* From flash-attention
*/
m.impl("fwd", torch::kCUDA, make_pytorch_shim(&mha_fwd));
```
### Testing & Benchmarking
1. Add pytest tests in [tests/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/tests), if you need to skip some test, please use `@pytest.mark.skipif`
```python
@pytest.mark.skipif(
skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
)
```
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)
3. 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](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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LICENSE
README.md
pyproject.toml
setup_hip.py
csrc/common_extension_rocm.cc
csrc/allreduce/custom_all_reduce.hip
csrc/allreduce/quick_all_reduce.hip
csrc/elementwise/activation.hip
csrc/grammar/apply_token_bitmask_inplace_cuda.hip
csrc/kvcacheio/transfer.hip
csrc/moe/moe_align_kernel.hip
csrc/moe/moe_topk_softmax_kernels.hip
csrc/speculative/eagle_utils.hip
python/sgl_kernel/__init__.py
python/sgl_kernel/allreduce.py
python/sgl_kernel/attention.py
python/sgl_kernel/cutlass_moe.py
python/sgl_kernel/elementwise.py
python/sgl_kernel/flash_attn.py
python/sgl_kernel/fused_moe.py
python/sgl_kernel/gemm.py
python/sgl_kernel/grammar.py
python/sgl_kernel/kvcacheio.py
python/sgl_kernel/marlin.py
python/sgl_kernel/memory.py
python/sgl_kernel/moe.py
python/sgl_kernel/sampling.py
python/sgl_kernel/scalar_type.py
python/sgl_kernel/sparse_flash_attn.py
python/sgl_kernel/spatial.py
python/sgl_kernel/speculative.py
python/sgl_kernel/top_k.py
python/sgl_kernel/utils.py
python/sgl_kernel/version.py
python/sgl_kernel.egg-info/PKG-INFO
python/sgl_kernel.egg-info/SOURCES.txt
python/sgl_kernel.egg-info/dependency_links.txt
python/sgl_kernel.egg-info/top_level.txt
python/sgl_kernel/testing/__init__.py
python/sgl_kernel/testing/rotary_embedding.py
tests/test_activation.py
tests/test_apply_token_bitmask_inplace.py
tests/test_awq_dequant.py
tests/test_bmm_fp8.py
tests/test_custom_allreduce.py
tests/test_cutlass_mla.py
tests/test_cutlass_w4a8_moe_mm.py
tests/test_dsv3_fused_a_gemm.py
tests/test_dsv3_router_gemm.py
tests/test_ep_moe_post_reorder_kernel.py
tests/test_ep_moe_pre_reorder_kernel.py
tests/test_ep_moe_silu_and_mul_kernel.py
tests/test_flash_attention.py
tests/test_fp4_gemm.py
tests/test_fp4_quantize.py
tests/test_fp8_blockwise_gemm.py
tests/test_fp8_blockwise_moe.py
tests/test_fp8_gemm.py
tests/test_gptq_kernel.py
tests/test_int8_gemm.py
tests/test_kvcacheio.py
tests/test_lightning_attention_decode.py
tests/test_marlin_gemm.py
tests/test_marlin_repack.py
tests/test_merge_state.py
tests/test_merge_state_v2.py
tests/test_moe_align.py
tests/test_moe_fused_gate.py
tests/test_moe_topk_softmax.py
tests/test_mscclpp.py
tests/test_norm.py
tests/test_per_tensor_quant_fp8.py
tests/test_per_token_group_quant_8bit.py
tests/test_per_token_quant_fp8.py
tests/test_qserve_w4a8_per_chn_gemm.py
tests/test_qserve_w4a8_per_group_gemm.py
tests/test_rotary_embedding.py
tests/test_sampling.py
tests/test_sparse_flash_attn.py

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