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Unverified Commit 8bf6d7f4 authored by Yineng Zhang's avatar Yineng Zhang Committed by GitHub
Browse files

support cmake for sgl-kernel (#4706) 


Co-authored-by: default avatarhebiao064 <hebiaobuaa@gmail.com>
Co-authored-by: default avataryinfan98 <1106310035@qq.com>
parent 1b9175cb
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Showing with 425 additions and 35 deletions
.github/workflows/pr-test-amd.yml
View file @ 8bf6d7f4
......@@ -47,7 +47,7 @@ jobs:
run: |
docker exec ci_sglang pip install --upgrade pip
docker exec ci_sglang pip uninstall sgl-kernel -y || true
docker exec -w /sglang-checkout/sgl-kernel ci_sglang python3 setup_rocm.py install
docker exec -w /sglang-checkout/sgl-kernel ci_sglang bash -c "rm -f pyproject.toml && mv pyproject_rocm.toml pyproject.toml && python3 setup_rocm.py install"
docker exec ci_sglang pip install -e "python[dev_hip]"
docker exec -w / ci_sglang git clone https://github.com/merrymercy/human-eval.git
......@@ -87,7 +87,7 @@ jobs:
run: |
docker exec ci_sglang pip install --upgrade pip
docker exec ci_sglang pip uninstall sgl-kernel -y || true
docker exec -w /sglang-checkout/sgl-kernel ci_sglang python3 setup_rocm.py install
docker exec -w /sglang-checkout/sgl-kernel ci_sglang bash -c "rm -f pyproject.toml && mv pyproject_rocm.toml pyproject.toml && python3 setup_rocm.py install"
docker exec ci_sglang pip install -e "python[dev_hip]"
docker exec -w / ci_sglang git clone https://github.com/merrymercy/human-eval.git
......
.github/workflows/pr-test-sgl-kernel.yml
View file @ 8bf6d7f4
......@@ -84,6 +84,7 @@ jobs:
pip3 uninstall sgl-kernel -y || true
pip3 install sgl-kernel/dist/*whl --force-reinstall --no-deps
pip3 list | grep sgl-kernel
git clone --recursive https://github.com/deepseek-ai/DeepGEMM.git && cd DeepGEMM && python3 setup.py develop
- name: Run test
timeout-minutes: 30
......@@ -115,6 +116,7 @@ jobs:
pip3 uninstall sgl-kernel -y || true
pip3 install sgl-kernel/dist/*whl --force-reinstall --no-deps
pip3 list | grep sgl-kernel
git clone --recursive https://github.com/deepseek-ai/DeepGEMM.git && cd DeepGEMM && python3 setup.py develop
- name: Run test
timeout-minutes: 30
......
docker/Dockerfile.rocm
View file @ 8bf6d7f4
......@@ -29,6 +29,8 @@ RUN git clone ${SGL_REPO} \
git checkout ${SGL_BRANCH}; \
fi \
&& cd sgl-kernel \
&& rm -f pyproject.toml \
&& mv pyproject_rocm.toml pyproject.toml \
&& python setup_rocm.py install \
&& cd .. \
&& if [ "$BUILD_TYPE" = "srt" ]; then \
......
flashinfer @ 79fd1ae9
Compare e5a3befb...79fd1ae9
Subproject commit e5a3befbe3e63025f0158bc96b218a9c5f402ac7
Subproject commit 79fd1ae90d9b8098ca70dec6071da96f3f6da7b9
sgl-kernel/CMakeLists.txt 0 → 100644
View file @ 8bf6d7f4
cmake_minimum_required(VERSION 3.26 FATAL_ERROR)
project(sgl-kernel LANGUAGES CXX CUDA)
# we only want to download 3rd, but not build them.
# FetchContent_MakeAvailable will build it.
cmake_policy(SET CMP0169 OLD)
find_package(Python COMPONENTS Interpreter Development.Module Development.SABIModule REQUIRED)
enable_language(CUDA)
find_package(CUDAToolkit REQUIRED)
message(STATUS "Detected CUDA_VERSION=${CUDA_VERSION}")
if ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "12.8")
message("CUDA_VERSION ${CUDA_VERSION} >= 12.8")
elseif ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "12.4")
message("CUDA_VERSION ${CUDA_VERSION} >= 12.4")
elseif ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "12.1")
message("CUDA_VERSION ${CUDA_VERSION} >= 12.1")
elseif ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "11.8")
message("CUDA_VERSION ${CUDA_VERSION} >= 11.8")
endif()
find_package(Torch REQUIRED)
include(FetchContent)
FetchContent_Declare(
repo-cutlass
GIT_REPOSITORY https://github.com/NVIDIA/cutlass
GIT_TAG 62750a2b75c802660e4894434dc55e839f322277
GIT_SHALLOW ON
)
FetchContent_Populate(repo-cutlass)
FetchContent_Declare(
repo-deepgemm
GIT_REPOSITORY https://github.com/deepseek-ai/DeepGEMM
GIT_TAG c57699ac933a93651c34d365797c2d8b41a4765b
GIT_SHALLOW ON
)
FetchContent_Populate(repo-deepgemm)
FetchContent_Declare(
repo-flashinfer
GIT_REPOSITORY https://github.com/flashinfer-ai/flashinfer
GIT_TAG 79fd1ae90d9b8098ca70dec6071da96f3f6da7b9
GIT_SHALLOW OFF
)
FetchContent_Populate(repo-flashinfer)
include_directories(
${PROJECT_SOURCE_DIR}/include
${PROJECT_SOURCE_DIR}/csrc
${repo-cutlass_SOURCE_DIR}/include
${repo-cutlass_SOURCE_DIR}/tools/util/include
${repo-flashinfer_SOURCE_DIR}/include
${repo-flashinfer_SOURCE_DIR}/csrc
)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
set(SGL_KERNEL_CUDA_FLAGS
"-DNDEBUG"
"-DOPERATOR_NAMESPACE=sgl-kernel"
"-O3"
"-Xcompiler"
"-fPIC"
"-gencode=arch=compute_75,code=sm_75"
"-gencode=arch=compute_80,code=sm_80"
"-gencode=arch=compute_89,code=sm_89"
"-gencode=arch=compute_90,code=sm_90"
"-std=c++17"
"-DFLASHINFER_ENABLE_F16"
"-DCUTLASS_ENABLE_TENSOR_CORE_MMA=1"
"-DCUTLASS_VERSIONS_GENERATED"
"-DCUTE_USE_PACKED_TUPLE=1"
"-DCUTLASS_TEST_LEVEL=0"
"-DCUTLASS_TEST_ENABLE_CACHED_RESULTS=1"
"-DCUTLASS_DEBUG_TRACE_LEVEL=0"
"--expt-relaxed-constexpr"
"-Xcompiler=-Wconversion"
"-Xcompiler=-fno-strict-aliasing"
)
option(SGL_KERNEL_ENABLE_SM100A "Enable SM100A" OFF)
option(SGL_KERNEL_ENABLE_SM90A "Enable SM90A" OFF)
option(SGL_KERNEL_ENABLE_BF16 "Enable BF16" ON)
option(SGL_KERNEL_ENABLE_FP8 "Enable FP8" ON)
if ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "12.8" OR SGL_KERNEL_ENABLE_SM100A)
list(APPEND SGL_KERNEL_CUDA_FLAGS
"-gencode=arch=compute_100,code=sm_100"
"-gencode=arch=compute_100a,code=sm_100a"
)
else()
list(APPEND SGL_KERNEL_CUDA_FLAGS
"-use_fast_math"
)
endif()
if ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "12.4" OR SGL_KERNEL_ENABLE_SM90A)
list(APPEND SGL_KERNEL_CUDA_FLAGS
"-gencode=arch=compute_90a,code=sm_90a"
)
endif()
if (SGL_KERNEL_ENABLE_BF16)
list(APPEND SGL_KERNEL_CUDA_FLAGS
"-DFLASHINFER_ENABLE_BF16"
)
endif()
if (SGL_KERNEL_ENABLE_FP8)
list(APPEND SGL_KERNEL_CUDA_FLAGS
"-DFLASHINFER_ENABLE_FP8"
"-DFLASHINFER_ENABLE_FP8_E4M3"
"-DFLASHINFER_ENABLE_FP8_E5M2"
)
endif()
string(REPLACE "-D__CUDA_NO_HALF_OPERATORS__" "" CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS}")
string(REPLACE "-D__CUDA_NO_HALF_CONVERSIONS__" "" CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS}")
string(REPLACE "-D__CUDA_NO_BFLOAT16_CONVERSIONS__" "" CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS}")
string(REPLACE "-D__CUDA_NO_HALF2_OPERATORS__" "" CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS}")
set(SOURCES
"csrc/allreduce/trt_reduce_internal.cu"
"csrc/allreduce/trt_reduce_kernel.cu"
"csrc/attention/lightning_attention_decode_kernel.cu"
"csrc/elementwise/activation.cu"
"csrc/elementwise/fused_add_rms_norm_kernel.cu"
"csrc/elementwise/rope.cu"
"csrc/gemm/awq_kernel.cu"
"csrc/gemm/bmm_fp8.cu"
"csrc/gemm/cublas_grouped_gemm.cu"
"csrc/gemm/fp8_blockwise_gemm_kernel.cu"
"csrc/gemm/fp8_gemm_kernel.cu"
"csrc/gemm/int8_gemm_kernel.cu"
"csrc/gemm/nvfp4_quant_entry.cu"
"csrc/gemm/nvfp4_quant_kernels.cu"
"csrc/gemm/nvfp4_scaled_mm_entry.cu"
"csrc/gemm/nvfp4_scaled_mm_kernels.cu"
"csrc/gemm/per_tensor_quant_fp8.cu"
"csrc/gemm/per_token_group_quant_8bit.cu"
"csrc/gemm/per_token_quant_fp8.cu"
"csrc/moe/moe_align_kernel.cu"
"csrc/moe/moe_topk_softmax_kernels.cu"
"csrc/speculative/eagle_utils.cu"
"csrc/speculative/speculative_sampling.cu"
"csrc/speculative/packbit.cu"
"csrc/torch_extension.cc"
"${repo-flashinfer_SOURCE_DIR}/csrc/norm.cu"
"${repo-flashinfer_SOURCE_DIR}/csrc/renorm.cu"
"${repo-flashinfer_SOURCE_DIR}/csrc/sampling.cu"
)
# Support abi3 for build
Python_add_library(common_ops MODULE USE_SABI 3.9 WITH_SOABI ${SOURCES})
target_compile_options(common_ops PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:${SGL_KERNEL_CUDA_FLAGS}>)
target_include_directories(common_ops PRIVATE ${TORCH_INCLUDE_DIRS})
target_link_libraries(common_ops PRIVATE ${TORCH_LIBRARIES} c10 cuda cublas cublasLt)
install(TARGETS common_ops LIBRARY DESTINATION "sgl_kernel")
sgl-kernel/Makefile
View file @ 8bf6d7f4
......@@ -19,13 +19,14 @@ submodule: ## Initialize and update git submodules
@git submodule update --init --recursive
ln: submodule ## Create compilation database
@rm -rf build && bear python3 setup.py build
@rm -rf build && mkdir build && cd build && cmake .. -DCMAKE_EXPORT_COMPILE_COMMANDS=YES
install: submodule ## Install package in development mode
@pip install -e .
build: submodule ## Build and install wheel package
@rm -rf dist/* || true && export MAX_JOBS=$(nproc) && python3 setup.py bdist_wheel && pip3 install dist/*whl --force-reinstall --no-deps
@rm -rf dist/* || true && export MAX_JOBS=$(nproc) && uv build --wheel -Cbuild-dir=build . --verbose --color=always && pip3 install dist/*whl --force-reinstall --no-deps
clean: ## Remove build artifacts
@rm -rf build dist *.egg-info
......
sgl-kernel/build.sh
View file @ 8bf6d7f4
......@@ -15,7 +15,7 @@ docker run --rm \
pytorch/manylinux-builder:cuda${CUDA_VERSION} \
bash -c "
${PYTHON_ROOT_PATH}/bin/pip install --no-cache-dir torch==2.5.1 --index-url https://download.pytorch.org/whl/cu${CUDA_VERSION//.} && \
${PYTHON_ROOT_PATH}/bin/pip install --no-cache-dir ninja setuptools==75.0.0 wheel==0.41.0 numpy && \
${PYTHON_ROOT_PATH}/bin/pip install --no-cache-dir ninja setuptools==75.0.0 wheel==0.41.0 numpy uv && \
export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0+PTX' && \
export CUDA_VERSION=${CUDA_VERSION} && \
export SGL_KERNEL_ENABLE_BF16=1 && \
......@@ -25,5 +25,5 @@ docker run --rm \
ln -s /usr/local/cuda-${CUDA_VERSION}/targets/x86_64-linux/lib/stubs/libcuda.so /usr/lib/x86_64-linux-gnu/libcuda.so && \
cd /sgl-kernel && \
ls -la ${PYTHON_ROOT_PATH}/lib/python${PYTHON_VERSION}/site-packages/wheel/ && \
PYTHONPATH=${PYTHON_ROOT_PATH}/lib/python${PYTHON_VERSION}/site-packages ${PYTHON_ROOT_PATH}/bin/python setup.py bdist_wheel
PYTHONPATH=${PYTHON_ROOT_PATH}/lib/python${PYTHON_VERSION}/site-packages ${PYTHON_ROOT_PATH}/bin/python -m uv build --wheel -Cbuild-dir=build . --color=always
"
sgl-kernel/csrc/attention/lightning_attention_decode_kernel.cu
View file @ 8bf6d7f4
......@@ -18,7 +18,7 @@ limitations under the License.
#include <c10/cuda/CUDAGuard.h>
#include <cuda_fp16.h>
#include <cuda_runtime.h>
#include <torch/extension.h>
#include <torch/all.h>
#define THREADS_PER_BLOCK 128
......
sgl-kernel/csrc/gemm/cublas_grouped_gemm.cu
View file @ 8bf6d7f4
......@@ -12,7 +12,6 @@
#include <cuda_fp16.h>
#include <cuda_runtime.h>
#include <torch/all.h>
#include <torch/extension.h>
#include <cstdio>
#include <cstdlib>
......
sgl-kernel/csrc/moe/moe_align_kernel.cu
View file @ 8bf6d7f4
......@@ -16,7 +16,6 @@ limitations under the License.
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/extension.h>
#include <THC/THCAtomics.cuh>
......
sgl-kernel/csrc/torch_extension.cc
View file @ 8bf6d7f4
......@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <ATen/core/dispatch/Dispatcher.h>
#include <torch/all.h>
#include <torch/library.h>
#include "sgl_kernel_ops.h"
......@@ -178,9 +178,9 @@ TORCH_LIBRARY_EXPAND(sgl_kernel, m) {
m.impl("min_p_sampling_from_probs", torch::kCUDA, &min_p_sampling_from_probs);
m.def(
"top_k_renorm_probs_wrapper(Tensor probs, Tensor! renorm_probs, Tensor? maybe_top_k_arr, int top_k_val, int "
"top_k_renorm_probs(Tensor probs, Tensor! renorm_probs, Tensor? maybe_top_k_arr, int top_k_val, int "
"cuda_stream) -> ()");
m.impl("top_k_renorm_probs_wrapper", torch::kCUDA, &top_k_renorm_probs_wrapper);
m.impl("top_k_renorm_probs", torch::kCUDA, &top_k_renorm_probs);
m.def(
"top_p_renorm_probs(Tensor probs, Tensor! renorm_probs, Tensor? maybe_top_p_arr, float top_p_val, int "
......
sgl-kernel/include/sgl_kernel_ops.h
View file @ 8bf6d7f4
......@@ -15,8 +15,11 @@ limitations under the License.
#pragma once
#include <ATen/ATen.h>
#include <ATen/Tensor.h>
#include <Python.h>
#include <torch/extension.h>
#include <torch/library.h>
#include <torch/torch.h>
#include <vector>
......@@ -253,23 +256,12 @@ void min_p_sampling_from_probs(
double min_p_val,
bool deterministic,
int64_t cuda_stream);
// top k renorm probs
// patch here, cause flashinfer use unsigned int. but torch must use int64_t for extension.
void top_k_renorm_probs(
at::Tensor probs,
at::Tensor renorm_probs,
std::optional<at::Tensor> maybe_top_k_arr,
unsigned int top_k_val,
int64_t cuda_stream);
// patch here, cause flashinfer use unsigned int. but torch must use int64_t for extension.
inline void top_k_renorm_probs_wrapper(
at::Tensor probs,
at::Tensor renorm_probs,
std::optional<at::Tensor> maybe_top_k_arr,
int64_t top_k_val,
int64_t cuda_stream) {
top_k_renorm_probs(probs, renorm_probs, maybe_top_k_arr, static_cast<unsigned int>(top_k_val), cuda_stream);
}
int64_t cuda_stream);
void top_p_renorm_probs(
at::Tensor probs,
at::Tensor renorm_probs,
......
sgl-kernel/include/utils.h
View file @ 8bf6d7f4
......@@ -15,13 +15,189 @@ limitations under the License.
#pragma once
#include <ATen/Tensor.h>
#include <cuda_runtime.h>
#include <torch/all.h>
#include <sstream>
#ifndef USE_ROCM
#include <pytorch_extension_utils.h>
// Adapt from FlashInfer
#ifdef FLASHINFER_ENABLE_F16
#define _DISPATCH_CASE_F16(c_type, ...) \
case at::ScalarType::Half: { \
using c_type = nv_half; \
return __VA_ARGS__(); \
}
#else
#define _DISPATCH_CASE_F16(c_type, ...)
#endif
#include <torch/extension.h>
#include <sstream>
#ifdef FLASHINFER_ENABLE_BF16
#define _DISPATCH_CASE_BF16(c_type, ...) \
case at::ScalarType::BFloat16: { \
using c_type = nv_bfloat16; \
return __VA_ARGS__(); \
}
#else
#define _DISPATCH_CASE_BF16(c_type, ...)
#endif
#ifdef FLASHINFER_ENABLE_FP8_E4M3
#define _DISPATCH_CASE_FP8_E4M3(c_type, ...) \
case at::ScalarType::Float8_e4m3fn: { \
using c_type = __nv_fp8_e4m3; \
return __VA_ARGS__(); \
}
#else
#define _DISPATCH_CASE_FP8_E4M3(c_type, ...)
#endif
#ifdef FLASHINFER_ENABLE_FP8_E5M2
#define _DISPATCH_CASE_FP8_E5M2(c_type, ...) \
case at::ScalarType::Float8_e5m2: { \
using c_type = __nv_fp8_e5m2; \
return __VA_ARGS__(); \
}
#else
#define _DISPATCH_CASE_FP8_E5M2(c_type, ...)
#endif
#define DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(pytorch_dtype, c_type, ...) \
[&]() -> bool { \
switch (pytorch_dtype) { \
_DISPATCH_CASE_F16(c_type, __VA_ARGS__) \
_DISPATCH_CASE_BF16(c_type, __VA_ARGS__) \
default: \
std::ostringstream oss; \
oss << __PRETTY_FUNCTION__ << " failed to dispatch data type " << pytorch_dtype; \
TORCH_CHECK(false, oss.str()); \
return false; \
} \
}()
#define DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP8(pytorch_dtype, c_type, ...) \
[&]() -> bool { \
switch (pytorch_dtype) { \
_DISPATCH_CASE_FP8_E4M3(c_type, __VA_ARGS__) \
_DISPATCH_CASE_FP8_E5M2(c_type, __VA_ARGS__) \
default: \
std::ostringstream oss; \
oss << __PRETTY_FUNCTION__ << " failed to dispatch fp8 data type " << pytorch_dtype; \
TORCH_CHECK(false, oss.str()); \
return false; \
} \
}()
#define DISPATCH_PYTORCH_DTYPE_TO_CTYPE(pytorch_dtype, c_type, ...) \
[&]() -> bool { \
switch (pytorch_dtype) { \
_DISPATCH_CASE_F16(c_type, __VA_ARGS__) \
_DISPATCH_CASE_BF16(c_type, __VA_ARGS__) \
_DISPATCH_CASE_FP8_E4M3(c_type, __VA_ARGS__) \
_DISPATCH_CASE_FP8_E5M2(c_type, __VA_ARGS__) \
default: \
std::ostringstream oss; \
oss << __PRETTY_FUNCTION__ << " failed to dispatch data type " << pytorch_dtype; \
TORCH_CHECK(false, oss.str()); \
return false; \
} \
}()
#define _DISPATCH_SWITCH(var_name, cond, ...) \
[&]() -> bool { \
switch (cond) { \
__VA_ARGS__ \
default: \
std::ostringstream oss; \
oss << __PRETTY_FUNCTION__ << " failed to dispatch " var_name " " << int(cond); \
TORCH_CHECK(false, oss.str()); \
return false; \
} \
}()
#define _DISPATCH_SWITCH_U16x2(var1_name, var2_name, cond1, cond2, ...) \
[&]() -> bool { \
switch (pack_u16(cond1, cond2)) { \
__VA_ARGS__ \
default: \
std::ostringstream oss; \
oss << __PRETTY_FUNCTION__ << " failed to dispatch (" var1_name ", " var2_name "): (" << int(cond1) << ", " \
<< int(cond2) << ")"; \
TORCH_CHECK(false, oss.str()); \
return false; \
} \
}()
#define _DISPATCH_CASE(case_expr, case_var, ...) \
case case_expr: { \
constexpr auto case_var = case_expr; \
return __VA_ARGS__(); \
}
#define _DISPATCH_CASE_U16x2(case_expr1, case_expr2, case_var1, case_var2, ...) \
case pack_u16(case_expr1, case_expr2): { \
constexpr auto case_var1 = case_expr1; \
constexpr auto case_var2 = case_expr2; \
return __VA_ARGS__(); \
}
#define DISPATCH_BOOL(expr, const_expr, ...) \
[&]() -> bool { \
if (expr) { \
constexpr bool const_expr = true; \
return __VA_ARGS__(); \
} else { \
constexpr bool const_expr = false; \
return __VA_ARGS__(); \
} \
}()
inline void check_shape(const at::Tensor& a, const at::Tensor& b, const char* a_name, const char* b_name) {
TORCH_CHECK(a.dim() == b.dim(), a_name, ".dim() != ", b_name, ".dim(). ", a.dim(), " vs ", b.dim());
for (int i = 0; i < a.dim(); ++i) {
TORCH_CHECK(a.size(i) == b.size(i), a_name, ".size(", i, ") != ", b_name, ".size(", i, ")");
}
}
inline constexpr uint32_t pack_u16(uint16_t a, uint16_t b) {
return (uint32_t(a) << 16) | uint32_t(b);
}
#define CHECK_GQA_HEAD_DIVISIBLE(num_qo_heads, num_kv_heads) \
TORCH_CHECK( \
num_qo_heads % num_kv_heads == 0, \
"num_qo_heads(", \
num_qo_heads, \
") must be divisible by num_kv_heads(", \
num_kv_heads, \
")")
#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_LAST_DIM_CONTIGUOUS(x) \
TORCH_CHECK(x.strides()[x.strides().size() - 1] == 1, #x "must be contiguous at last dimension")
#define CHECK_INPUT(x) \
CHECK_CUDA(x); \
CHECK_CONTIGUOUS(x)
#define CHECK_LAST_DIM_CONTIGUOUS_INPUT(x) \
CHECK_CUDA(x); \
CHECK_LAST_DIM_CONTIGUOUS(x)
#define CHECK_DIM(d, x) TORCH_CHECK(x.dim() == d, #x " must be a " #d "D tensor")
#define CHECK_SHAPE(a, b) check_shape(a, b, #a, #b)
#define CHECK_EQ(a, b) TORCH_CHECK((a) == (b), "CHECK_EQ(" #a ", " #b ") failed. ", a, " vs ", b)
#define CHECK_GE(a, b) TORCH_CHECK((a) >= (b), "CHECK_GE(" #a ", " #b ") failed. ", a, " vs ", b)
inline bool is_float8_tensor(const at::Tensor& tensor) {
return tensor.scalar_type() == at::ScalarType::Float8_e4m3fn || tensor.scalar_type() == at::ScalarType::Float8_e5m2;
}
#endif
struct cuda_error : public std::runtime_error {
/**
......
sgl-kernel/pyproject.toml
View file @ 8bf6d7f4
[build-system]
requires = [
"setuptools>=75.0",
"scikit-build-core>=0.10",
"torch==2.5.1",
"wheel",
]
build-backend = "setuptools.build_meta"
build-backend = "scikit_build_core.build"
[project]
name = "sgl-kernel"
......@@ -30,3 +29,11 @@ exclude = [
"dist*",
"tests*",
]
[tool.scikit-build]
cmake.build-type = "Release"
minimum-version = "build-system.requires"
wheel.py-api = "cp39"
wheel.license-files = []
wheel.packages = ["python/sgl_kernel"]
sgl-kernel/pyproject_rocm.toml 0 → 100644
View file @ 8bf6d7f4
[build-system]
requires = [
"setuptools>=75.0",
"scikit-build-core>=0.10",
"torch==2.5.1",
"wheel",
]
build-backend = "setuptools.build_meta"
[project]
name = "sgl-kernel"
version = "0.0.5.post3"
description = "Kernel Library for SGLang"
readme = "README.md"
requires-python = ">=3.9"
license = { file = "LICENSE" }
classifiers = [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: Apache Software License",
"Environment :: GPU :: NVIDIA CUDA"
]
dependencies = []
[project.urls]
"Homepage" = "https://github.com/sgl-project/sglang/tree/main/sgl-kernel"
"Bug Tracker" = "https://github.com/sgl-project/sglang/issues"
[tool.wheel]
exclude = [
"dist*",
"tests*",
]
sgl-kernel/python/sgl_kernel/sampling.py
View file @ 8bf6d7f4
......@@ -12,7 +12,7 @@ def _top_k_renorm_probs_internal(
probs = probs.float()
maybe_top_k_arr = maybe_top_k_arr.int() if maybe_top_k_arr is not None else None
renorm_probs = torch.empty_like(probs)
torch.ops.sgl_kernel.top_k_renorm_probs_wrapper(
torch.ops.sgl_kernel.top_k_renorm_probs(
probs,
renorm_probs,
maybe_top_k_arr,
......
sgl-kernel/rename_wheels.sh 0 → 100755
View file @ 8bf6d7f4
#!/usr/bin/env bash
set -ex
WHEEL_DIR="dist"
wheel_files=($WHEEL_DIR/*.whl)
for wheel in "${wheel_files[@]}"; do
new_wheel="${wheel/linux/manylinux2014}"
if [[ "$wheel" != "$new_wheel" ]]; then
echo "Renaming $wheel to $new_wheel"
mv -- "$wheel" "$new_wheel"
fi
done
echo "Wheel renaming completed."
sgl-kernel/setup_rocm.py
View file @ 8bf6d7f4
......@@ -21,9 +21,6 @@ from torch.utils.cpp_extension import BuildExtension, CUDAExtension
root = Path(__file__).parent.resolve()
if "bdist_wheel" in sys.argv and "--plat-name" not in sys.argv:
sys.argv.extend(["--plat-name", "manylinux2014_x86_64"])
def _get_version():
with open(root / "pyproject.toml") as f:
......
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