EngineX-Hygon/sglang
5
0
Fork 0
Code Issues Pull Requests Actions 7 Projects Releases Wiki Activity

Add optimized native kernels in sgl-kernel (#5150)

Browse Source 
Co-authored-by: Chunyuan WU <chunyuan.wu@intel.com>
Co-authored-by: YanbingJiang <yanbing.jiang@intel.com>
Co-authored-by: blzheng <beilei.zheng@intel.com>
This commit is contained in:
Ma Mingfei
2025-04-09 00:37:46 +08:00
committed by GitHub
parent 89a554181f
commit a73c4df438
20 changed files with 7792 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

122
sgl-kernel/csrc/cpu/bmm.cpp Normal file
View File

@@ -0,0 +1,122 @@
#include "common.h"
#include "gemm.h"
#include "vec.h"
namespace {
template <typename scalar_t>
void bmm_kernel_impl(
scalar_t* __restrict__ out,
const scalar_t* __restrict__ mat1,
const scalar_t* __restrict__ mat2,
int64_t B,
int64_t M,
int64_t N,
int64_t K,
int64_t mat1_strideB,
int64_t mat1_strideM,
int64_t out_strideB,
int64_t out_strideM,
float scale = 0.f) {
constexpr int64_t BLOCK_M = block_size_m();
constexpr int64_t BLOCK_N = block_size_n();
const int64_t MB = div_up(M, BLOCK_M);
const int64_t NB = div_up(N, BLOCK_N);
// mat2 contiguous in [B, N, K]
int64_t mat2_strideB = N * K;
int64_t mat2_strideN = K;
const bool use_brgemm = can_use_brgemm<scalar_t>(M);
// parallel on [B, MB, NB]
at::parallel_for(0, B * MB * NB, 0, [&](int64_t begin, int64_t end) {
int64_t bs{0}, mb{0}, nb{0};
data_index_init(begin, bs, B, mb, MB, nb, NB);
// for brgemm, use float32 for accumulate
alignas(64) float Ctmp[BLOCK_M * BLOCK_N];
for (int i = begin; i < end; ++i) {
UNUSED(i);
int mb_start = mb * BLOCK_M;
int mb_size = std::min(M - mb_start, BLOCK_M);
int nb_start = nb * BLOCK_N;
int nb_size = std::min(N - nb_start, BLOCK_N);
tinygemm_kernel<scalar_t>(
/* A */ mat1 + bs * mat1_strideB + mb_start * mat1_strideM,
/* B */ mat2 + bs * mat2_strideB + nb_start * mat2_strideN /* nb * BLOCK_N * K */,
/* C */ out + bs * out_strideB + mb_start * out_strideM + nb_start,
/* Ctmp*/ Ctmp,
/* M */ mb_size,
/* N */ nb_size,
/* K */ K,
/* lda */ mat1_strideM,
/* ldb */ nb_size,
/* ldc */ out_strideM,
/* brg */ use_brgemm);
// move to the next index
data_index_step(bs, B, mb, MB, nb, NB);
}
if (use_brgemm) {
at::native::cpublas::brgemm_release();
}
});
}
} // anonymous namespace
// mat1 : [B, M, K]
// mat2 : [B, N, K] or [B, OC, IC]
// out : [B, M, N]
// scale: [] 0-dim tensor for per tensor quant
//
void bmm_cpu(at::Tensor& out, at::Tensor& mat1, at::Tensor& mat2, bool is_vnni, std::optional<at::Tensor>& scale) {
RECORD_FUNCTION("sgl-kernel::bmm_cpu", std::vector<c10::IValue>({out, mat1, mat2}));
auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
// input and out could be non-contiguous
// weight needs to be contiguous in [OC, IC] order
CHECK_LAST_DIM_CONTIGUOUS_INPUT(mat1);
CHECK_LAST_DIM_CONTIGUOUS_INPUT(out);
CHECK_INPUT(mat2);
CHECK_DIM(3, out);
CHECK_DIM(3, mat1);
CHECK_DIM(3, mat2);
int64_t B = mat1.size(0);
int64_t M = mat1.size(1);
int64_t N = mat2.size(1);
int64_t K = mat1.size(2);
TORCH_CHECK(!scale.has_value(), "bmm: do not support fp8 weight for now.")
TORCH_CHECK(N % 32 == 0, "tinygemm requires N to be 32x.");
int64_t mat1_strideB = mat1.stride(0);
int64_t mat1_strideM = mat1.stride(1);
int64_t out_strideB = out.stride(0);
int64_t out_strideM = out.stride(1);
// check shapes
TORCH_CHECK(mat2.size(0) == B && mat2.size(2) == K, "bmm: mat2 shape mismatch!");
TORCH_CHECK(out.size(0) == B && out.size(1) == M, "bmm: out shape mismatch!");
AT_DISPATCH_REDUCED_FLOATING_TYPES(mat1.scalar_type(), "bmm_kernel_impl", [&] {
bmm_kernel_impl<scalar_t>(
out.data_ptr<scalar_t>(),
mat1.data_ptr<scalar_t>(),
packed_w.data_ptr<scalar_t>(),
B,
M,
N,
K,
mat1_strideB,
mat1_strideM,
out_strideB,
out_strideM);
});
}