feat: add flashinfer as 3rdparty and use rmsnorm as example (#3033)

.github/workflows/pr-test-sgl-kernel.yml

@@ -41,6 +41,7 @@ jobs:
       - name: Install
         run: |
           pip3 install torch==2.5.1
+          pip3 install pytest
           pip3 uninstall sgl-kernel -y || true
           cd sgl-kernel
           pip3 install .

.gitignore

@@ -225,3 +225,5 @@ compile_commands.json
 
 # VSCode
 .vscode
+
+1

.gitmodules

@@ -4,3 +4,6 @@
 [submodule "sgl-kernel/3rdparty/cccl"]
 	path = sgl-kernel/3rdparty/cccl
 	url = https://github.com/NVIDIA/cccl.git
+[submodule "sgl-kernel/3rdparty/flashinfer"]
+	path = sgl-kernel/3rdparty/flashinfer
+	url = https://github.com/flashinfer-ai/flashinfer.git

flashinfer @ a0e99a3a

+Subproject commit a0e99a3a820109763d9a757138a5cdf7bbcd1f85

sgl-kernel/THIRDPARTYNOTICES.txt

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+-------------------------------------------------------------------------------------------------
+Some of the code in this project are adapted from other open-source projects with different
+licenses. This product also bundles some third-party components under other open source licenses.
+This section summarizes those components and their licenses.
+See licenses/ for text of these licenses.
+
+BSD 3-Clause License
+--------------------
+
+include/flashinfer/attention/hopper/epilogue.cuh
+include/flashinfer/attention/hopper/mainloop.cuh
+include/flashinfer/attention/hopper/kernel_traits.cuh
+include/flashinfer/attention/hopper/named_barrier.cuh
+include/flashinfer/attention/hopper/tile_scheduler.cuh
+include/flashinfer/attention/hopper/utils.cuh
+
+BSD 3-Clause "New" License
+--------------------------
+
+3rdparty/cutlass
+include/flashinfer/attention/hopper/block_sparse_gather.cuh

sgl-kernel/setup.py

 from pathlib import Path
 
+import torch
 from setuptools import find_packages, setup
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension
 
@@ -24,10 +25,13 @@ def update_wheel_platform_tag():
 
 
 cutlass = root / "3rdparty" / "cutlass"
+flashinfer = root / "3rdparty" / "flashinfer"
 include_dirs = [
     cutlass.resolve() / "include",
     cutlass.resolve() / "tools" / "util" / "include",
     root / "src" / "sgl-kernel" / "csrc",
+    flashinfer.resolve() / "include",
+    flashinfer.resolve() / "csrc",
 ]
 nvcc_flags = [
     "-DNDEBUG",
@@ -39,9 +43,21 @@ nvcc_flags = [
     "-gencode=arch=compute_89,code=sm_89",
     "-gencode=arch=compute_90,code=sm_90",
     "-gencode=arch=compute_90a,code=sm_90a",
-    "-U__CUDA_NO_HALF_OPERATORS__",
-    "-U__CUDA_NO_HALF2_OPERATORS__",
+    "-std=c++17",
+    "-use_fast_math",
+    "-DFLASHINFER_ENABLE_F16",
+    "-DFLASHINFER_ENABLE_BF16",
 ]
+for flag in [
+    "-D__CUDA_NO_HALF_OPERATORS__",
+    "-D__CUDA_NO_HALF_CONVERSIONS__",
+    "-D__CUDA_NO_BFLOAT16_CONVERSIONS__",
+    "-D__CUDA_NO_HALF2_OPERATORS__",
+]:
+    try:
+        torch.utils.cpp_extension.COMMON_NVCC_FLAGS.remove(flag)
+    except ValueError:
+        pass
 cxx_flags = ["-O3"]
 libraries = ["c10", "torch", "torch_python", "cuda"]
 extra_link_args = ["-Wl,-rpath,$ORIGIN/../../torch/lib", "-L/usr/lib/x86_64-linux-gnu"]
@@ -56,6 +72,7 @@ ext_modules = [
             "src/sgl-kernel/csrc/sampling_scaling_penalties.cu",
             "src/sgl-kernel/csrc/sgl_kernel_ops.cu",
             "src/sgl-kernel/csrc/rotary_embedding.cu",
+            "src/sgl-kernel/csrc/norm.cu",
         ],
         include_dirs=include_dirs,
         extra_compile_args={

sgl-kernel/src/sgl-kernel/__init__.py

@@ -6,6 +6,7 @@ from sgl_kernel.ops import (
     int8_scaled_mm,
     moe_align_block_size,
     register_graph_buffers,
+    rmsnorm,
     rotary_embedding,
     sampling_scaling_penalties,
 )
@@ -20,4 +21,5 @@ __all__ = [
     "get_graph_buffer_ipc_meta",
     "register_graph_buffers",
     "rotary_embedding",
+    "rmsnorm",
 ]

sgl-kernel/src/sgl-kernel/csrc/norm.cu

+#include <cstdint>
+#include <flashinfer/norm.cuh>
+
+#include "pytorch_extension_utils.h"
+
+using namespace flashinfer;
+
+void rmsnorm(at::Tensor& output, at::Tensor& input, at::Tensor& weight, double eps, int64_t cuda_stream) {
+  CHECK_INPUT(input);
+  CHECK_INPUT(weight);
+  auto device = input.device();
+  CHECK_EQ(weight.device(), device);
+  CHECK_DIM(2, input);   // input: (batch_size, hidden_size)
+  CHECK_DIM(1, weight);  // weight: (hidden_size)
+  CHECK_EQ(input.size(1), weight.size(0));
+  unsigned int batch_size = input.size(0);
+  unsigned int hidden_size = input.size(1);
+  CHECK_EQ(output.size(0), batch_size);
+  CHECK_EQ(output.size(1), hidden_size);
+
+  cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
+  DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(input.scalar_type(), c_type, [&] {
+    cudaError_t status = norm::RMSNorm(static_cast<c_type*>(input.data_ptr()), static_cast<c_type*>(weight.data_ptr()),
+                                       static_cast<c_type*>(output.data_ptr()), batch_size, hidden_size, eps, stream);
+    TORCH_CHECK(status == cudaSuccess, "RMSNorm failed with error code " + std::string(cudaGetErrorString(status)));
+    return true;
+  });
+}

sgl-kernel/src/sgl-kernel/csrc/sgl_kernel_ops.cu

@@ -30,6 +30,9 @@ torch::Tensor int8_scaled_mm(const torch::Tensor& mat_a, const torch::Tensor& ma
 void rotary_embedding(torch::Tensor& positions, torch::Tensor& query, torch::Tensor& key, int64_t head_size,
                       torch::Tensor& cos_sin_cache, bool is_neox);
 
+// rms norm
+void rmsnorm(at::Tensor& output, at::Tensor& input, at::Tensor& weight, double eps, int64_t cuda_stream);
+
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   // trt_reduce
   m.def("init_custom_ar", &init_custom_ar, "init custom allreduce meta (CUDA)");
@@ -45,4 +48,6 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("int8_scaled_mm", &int8_scaled_mm, "INT8 scaled matmul (CUDA)");
   // rotary embedding
   m.def("rotary_embedding", &rotary_embedding, "Rotary Embedding (CUDA)");
+  // rms norm
+  m.def("rmsnorm", &rmsnorm, "RMSNorm (CUDA)");
 }

sgl-kernel/src/sgl-kernel/ops/__init__.py

+from typing import Optional
+
+import torch
 from sgl_kernel.ops._kernels import all_reduce as _all_reduce
 from sgl_kernel.ops._kernels import dispose as _dispose
 from sgl_kernel.ops._kernels import (
@@ -7,6 +10,7 @@ from sgl_kernel.ops._kernels import init_custom_ar as _init_custom_ar
 from sgl_kernel.ops._kernels import int8_scaled_mm as _int8_scaled_mm
 from sgl_kernel.ops._kernels import moe_align_block_size as _moe_align_block_size
 from sgl_kernel.ops._kernels import register_graph_buffers as _register_graph_buffers
+from sgl_kernel.ops._kernels import rmsnorm as _rmsnorm
 from sgl_kernel.ops._kernels import rotary_embedding as _rotary_embedding
 from sgl_kernel.ops._kernels import (
     sampling_scaling_penalties as _sampling_scaling_penalties,
@@ -76,3 +80,17 @@ def int8_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype, bias=None):
 
 def rotary_embedding(positions, query, key, head_size, cos_sin_cache, is_neox):
     return _rotary_embedding(positions, query, key, head_size, cos_sin_cache, is_neox)
+
+
+def rmsnorm(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    eps: float = 1e-6,
+    out: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    if out is None:
+        out = torch.empty_like(input)
+    stream = torch.cuda.current_stream().cuda_stream
+    stream_int = int(stream)
+    _rmsnorm(out, input, weight, eps, stream_int)
+    return out

sgl-kernel/tests/test_rmsnorm.py

+import pytest
+import torch
+from sgl_kernel import rmsnorm
+
+
+def llama_rms_norm(x, w, eps=1e-6):
+    orig_dtype = x.dtype
+    x = x.float()
+    variance = x.pow(2).mean(dim=-1, keepdim=True)
+    x = x * torch.rsqrt(variance + eps)
+    x = x * w.float()
+    x = x.to(orig_dtype)
+    return x
+
+
+@pytest.mark.parametrize("batch_size", [1, 19, 99, 989])
+@pytest.mark.parametrize("hidden_size", [111, 500, 1024, 3072, 3584, 4096, 8192, 16384])
+@pytest.mark.parametrize("dtype", [torch.float16])
+@pytest.mark.parametrize("specify_out", [True, False])
+def test_norm(batch_size, hidden_size, dtype, specify_out):
+    x = torch.randn(batch_size, hidden_size).to(0).to(dtype)
+    w = torch.randn(hidden_size).to(0).to(dtype)
+
+    y_ref = llama_rms_norm(x, w)
+    if specify_out:
+        y = torch.empty_like(x)
+        rmsnorm(x, w, out=y)
+    else:
+        y = rmsnorm(x, w)
+
+    torch.testing.assert_close(y_ref, y, rtol=1e-3, atol=1e-3)