Support compile sgl-kernel on cuda 13.0 (#9721)

sgl-kernel/CMakeLists.txt

@@ -78,7 +78,7 @@ FetchContent_Populate(repo-triton)
 FetchContent_Declare(
     repo-flashinfer
     GIT_REPOSITORY https://github.com/flashinfer-ai/flashinfer.git
-    GIT_TAG        9220fb3443b5a5d274f00ca5552f798e225239b7
+    GIT_TAG        018b551825c8e5579206e6eb9d3229fa679202b3
     GIT_SHALLOW    OFF
 )
 FetchContent_Populate(repo-flashinfer)
@@ -174,11 +174,28 @@ if ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "12.8" OR SGL_KERNEL_ENABLE_SM100A)
     list(APPEND SGL_KERNEL_CUDA_FLAGS
         "-gencode=arch=compute_100,code=sm_100"
         "-gencode=arch=compute_100a,code=sm_100a"
-        "-gencode=arch=compute_101,code=sm_101"
-        "-gencode=arch=compute_101a,code=sm_101a"
         "-gencode=arch=compute_120,code=sm_120"
         "-gencode=arch=compute_120a,code=sm_120a"
     )
+
+    # refer sm_121, sm_110 and sm_101 description  https://github.com/pytorch/pytorch/pull/156176
+    if ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "13.0")
+        list(APPEND SGL_KERNEL_CUDA_FLAGS
+            "-gencode=arch=compute_103,code=sm_103"
+            "-gencode=arch=compute_103a,code=sm_103a"
+            "-gencode=arch=compute_110,code=sm_110"
+            "-gencode=arch=compute_110a,code=sm_110a"
+            "-gencode=arch=compute_121,code=sm_121"
+            "-gencode=arch=compute_121a,code=sm_121a"
+            "--compress-mode=size"
+        )
+    else()
+        list(APPEND SGL_KERNEL_CUDA_FLAGS
+            "-gencode=arch=compute_101,code=sm_101"
+            "-gencode=arch=compute_101a,code=sm_101a"
+        )
+    endif()
+
 else()
     list(APPEND SGL_KERNEL_CUDA_FLAGS
         "-use_fast_math"
@@ -261,12 +278,6 @@ set(SOURCES
     "csrc/moe/cutlass_moe/w4a8/w4a8_moe_data.cu"
     "csrc/moe/cutlass_moe/w4a8/w4a8_grouped_mm_c3x.cu"
     "csrc/moe/marlin_moe_wna16/ops.cu"
-    "csrc/moe/marlin_moe_wna16/kernel_bf16_ku4.cu"
-    "csrc/moe/marlin_moe_wna16/kernel_bf16_ku4b8.cu"
-    "csrc/moe/marlin_moe_wna16/kernel_bf16_ku8b128.cu"
-    "csrc/moe/marlin_moe_wna16/kernel_fp16_ku4.cu"
-    "csrc/moe/marlin_moe_wna16/kernel_fp16_ku4b8.cu"
-    "csrc/moe/marlin_moe_wna16/kernel_fp16_ku8b128.cu"
     "csrc/moe/moe_align_kernel.cu"
     "csrc/moe/moe_fused_gate.cu"
     "csrc/moe/moe_topk_softmax_kernels.cu"

sgl-kernel/csrc/moe/marlin_moe_wna16/generate_kernels.py

@@ -9,6 +9,7 @@ import jinja2
 FILE_HEAD = """
 // auto generated by generate.py
 // clang-format off
+#pragma once
 
 #include "kernel.h"
 #include "marlin_template.h"
@@ -33,6 +34,17 @@ TEMPLATE = (
     "( MARLIN_KERNEL_PARAMS );"
 )
 
+KERNEL_FILE_TEMPLATE = (
+    "// auto generated by generate.py\n"
+    "// clang-format off\n"
+    "#pragma once\n\n"
+    "{% for kernel_file in kernel_files %}"
+    '#include "{{ kernel_file }}"\n'
+    "{% endfor %}"
+)
+
+KERNEL_FILE_NAME = "kernel_marlin.cuh"
+
 # int8 with zero point case (sglang::kU8) is also supported,
 # we don't add it to reduce wheel size.
 SCALAR_TYPES = ["sglang::kU4", "sglang::kU4B8", "sglang::kU8B128"]
@@ -48,11 +60,12 @@ DTYPES = ["fp16", "bf16"]
 
 
 def remove_old_kernels():
-    for filename in glob.glob(os.path.dirname(__file__) + "/kernel_*.cu"):
+    for filename in glob.glob(os.path.dirname(__file__) + "/kernel_*.cuh"):
         subprocess.call(["rm", "-f", filename])
 
 
 def generate_new_kernels():
+    kernel_files = set()
     for scalar_type, dtype in itertools.product(SCALAR_TYPES, DTYPES):
         has_zp = "B" not in scalar_type
         all_template_str_list = []
@@ -95,10 +108,20 @@ def generate_new_kernels():
 
         file_content = FILE_HEAD + "\n\n"
         file_content += "\n\n".join(all_template_str_list) + "\n\n}\n"
-        filename = f"kernel_{dtype}_{scalar_type[8:].lower()}.cu"
+        filename = f"kernel_{dtype}_{scalar_type[8:].lower()}.cuh"
 
         with open(os.path.join(os.path.dirname(__file__), filename), "w") as f:
             f.write(file_content)
+            kernel_files.add(filename)
+
+    kernel_files = list(kernel_files)
+    kernel_files.sort()
+
+    file_content = jinja2.Template(KERNEL_FILE_TEMPLATE).render(
+        kernel_files=kernel_files
+    )
+    with open(os.path.join(os.path.dirname(__file__), KERNEL_FILE_NAME), "w") as f:
+        f.write(file_content)
 
 
 if __name__ == "__main__":

sgl-kernel/csrc/moe/marlin_moe_wna16/kernel.h

+#pragma once
 
 #ifndef MARLIN_NAMESPACE_NAME
 #define MARLIN_NAMESPACE_NAME marlin_moe_wna16

sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_bf16_ku4.cu → sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_bf16_ku4.cuh

 // auto generated by generate.py
 // clang-format off
+#pragma once
 
 #include "kernel.h"
 #include "marlin_template.h"

sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_bf16_ku4b8.cu → sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_bf16_ku4b8.cuh

 // auto generated by generate.py
 // clang-format off
+#pragma once
 
 #include "kernel.h"
 #include "marlin_template.h"

sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_bf16_ku8b128.cu → sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_bf16_ku8b128.cuh

 // auto generated by generate.py
 // clang-format off
+#pragma once
 
 #include "kernel.h"
 #include "marlin_template.h"

sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_fp16_ku4.cu → sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_fp16_ku4.cuh

 // auto generated by generate.py
 // clang-format off
+#pragma once
 
 #include "kernel.h"
 #include "marlin_template.h"

sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_fp16_ku4b8.cu → sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_fp16_ku4b8.cuh

 // auto generated by generate.py
 // clang-format off
+#pragma once
 
 #include "kernel.h"
 #include "marlin_template.h"

sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_fp16_ku8b128.cu → sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_fp16_ku8b128.cuh

 // auto generated by generate.py
 // clang-format off
+#pragma once
 
 #include "kernel.h"
 #include "marlin_template.h"

sgl-kernel/csrc/moe/marlin_moe_wna16/kernel_marlin.cuh

+// auto generated by generate.py
+// clang-format off
+#pragma once
+
+#include "kernel_bf16_ku4.cuh"
+#include "kernel_bf16_ku4b8.cuh"
+#include "kernel_bf16_ku8b128.cuh"
+#include "kernel_fp16_ku4.cuh"
+#include "kernel_fp16_ku4b8.cuh"
+#include "kernel_fp16_ku8b128.cuh"

sgl-kernel/csrc/moe/marlin_moe_wna16/marlin_template.h

@@ -18,6 +18,8 @@
 /*
  * Adapted from https://github.com/IST-DASLab/marlin
  */
+#pragma once
+
 #ifndef MARLIN_NAMESPACE_NAME
 #define MARLIN_NAMESPACE_NAME marlin_moe_wna16
 #endif

sgl-kernel/csrc/moe/marlin_moe_wna16/ops.cu

@@ -24,6 +24,7 @@
 #endif
 
 #include "kernel.h"
+#include "kernel_marlin.cuh"
 
 #define STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t)                                        \
   static_assert(                                                                         \

sgl-kernel/csrc/moe/moe_topk_softmax_kernels.cu

@@ -23,6 +23,7 @@ limitations under the License.
 #ifndef USE_ROCM
 #include <cub/cub.cuh>
 #include <cub/util_type.cuh>
+#include <cuda/functional>
 #else
 #include <hipcub/hipcub.hpp>
 #include <hipcub/util_type.hpp>
@@ -33,6 +34,16 @@ limitations under the License.
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 
+// Define reduction operators based on CUDA version
+// CUDA 13 (12.9+) deprecated cub::Max/Min in favor of cuda::maximum/minimum
+#if CUDA_VERSION >= 12090
+using MaxReduceOp = cuda::maximum<>;
+using MinReduceOp = cuda::minimum<>;
+#else
+using MaxReduceOp = cub::Max;
+using MinReduceOp = cub::Min;
+#endif
+
 /// Aligned array type
 template <
     typename T,
@@ -72,7 +83,6 @@ __launch_bounds__(TPB) __global__
 
   const int thread_row_offset = blockIdx.x * num_cols;
 
-  cub::Sum sum;
   float threadData(-FLT_MAX);
 
   // Don't touch finished rows.
@@ -85,7 +95,7 @@ __launch_bounds__(TPB) __global__
     threadData = max(convert_to_float<T>(input[idx]), threadData);
   }
 
-  const float maxElem = BlockReduce(tmpStorage).Reduce(threadData, cub::Max());
+  const float maxElem = BlockReduce(tmpStorage).Reduce(threadData, MaxReduceOp());
 
   if (threadIdx.x == 0) {
     float_max = maxElem;
@@ -99,7 +109,7 @@ __launch_bounds__(TPB) __global__
     threadData += exp((convert_to_float<T>(input[idx]) - float_max));
   }
 
-  const auto Z = BlockReduce(tmpStorage).Reduce(threadData, sum);
+  const auto Z = BlockReduce(tmpStorage).Sum(threadData);
 
   if (threadIdx.x == 0) {
     normalizing_factor = 1.f / Z;