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[Move sgl-kernel Kernel to JIT] Add JIT concat MLA kernels (#17889)

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This commit is contained in:
Linyu Wu
2026-02-03 10:49:17 +08:00
committed by GitHub
parent 62004fd2be
commit 9b1619c148
4 changed files with 722 additions and 0 deletions
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163
python/sglang/jit_kernel/benchmark/bench_concat_mla.py Normal file
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import itertools
import torch
import triton
import triton.testing
from sgl_kernel import concat_mla_absorb_q as aot_absorb_q
from sgl_kernel import concat_mla_k as aot_k
from sglang.jit_kernel.benchmark.utils import is_in_ci
from sglang.jit_kernel.concat_mla import concat_mla_absorb_q as jit_absorb_q
from sglang.jit_kernel.concat_mla import concat_mla_k as jit_k
IS_CI = is_in_ci()
# Constants
NUM_LOCAL_HEADS = 128
QK_NOPE_HEAD_DIM = 128
QK_ROPE_HEAD_DIM = 64
K_HEAD_DIM = QK_NOPE_HEAD_DIM + QK_ROPE_HEAD_DIM
A_LAST_DIM = 512
B_LAST_DIM = 64
DTYPE = torch.bfloat16
DEVICE = "cuda"
def aot_concat_mla_k(k, k_nope, k_rope):
aot_k(k, k_nope, k_rope)
def jit_concat_mla_k(k, k_nope, k_rope):
jit_k(k, k_nope, k_rope)
def torch_concat_mla_k(k, k_nope, k_rope):
nope_head_dim = k_nope.shape[-1]
k[:, :, :nope_head_dim] = k_nope
k[:, :, nope_head_dim:] = k_rope.expand(-1, k.shape[1], -1)
def aot_concat_mla_absorb_q(a, b):
return aot_absorb_q(a, b)
def jit_concat_mla_absorb_q(a, b):
return jit_absorb_q(a, b)
def torch_concat_mla_absorb_q(a, b, out):
a_last_dim = a.shape[-1]
out[:, :, :a_last_dim] = a
out[:, :, a_last_dim:] = b
if IS_CI:
NUM_TOKENS_VALS = [256, 1024]
else:
NUM_TOKENS_VALS = [256, 512, 1024, 2048, 4096, 8192, 16384, 32768]
K_LINE_VALS = ["aot", "jit", "torch"]
K_LINE_NAMES = ["SGL AOT Kernel", "SGL JIT Kernel", "PyTorch"]
K_STYLES = [("orange", "-"), ("blue", "--"), ("green", "-.")]
def _create_concat_mla_k_data(num_tokens):
"""Allocate oversized containers and slice to produce non-contiguous tensors."""
k_nope_container = torch.randn(
(num_tokens, NUM_LOCAL_HEADS, QK_NOPE_HEAD_DIM + 128),
dtype=DTYPE,
device=DEVICE,
)
k_nope = k_nope_container[:, :, :QK_NOPE_HEAD_DIM]
k_rope_container = torch.randn(
(num_tokens, 1, 128 + QK_ROPE_HEAD_DIM),
dtype=DTYPE,
device=DEVICE,
)
k_rope = k_rope_container[:, :, -QK_ROPE_HEAD_DIM:]
k = torch.empty(
(num_tokens, NUM_LOCAL_HEADS, K_HEAD_DIM),
dtype=DTYPE,
device=DEVICE,
)
return k, k_nope, k_rope
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=["num_tokens"],
x_vals=NUM_TOKENS_VALS,
line_arg="provider",
line_vals=K_LINE_VALS,
line_names=K_LINE_NAMES,
styles=K_STYLES,
ylabel="us",
plot_name="concat-mla-k-performance",
args={},
)
)
def bench_concat_mla_k(num_tokens: int, provider: str):
k, k_nope, k_rope = _create_concat_mla_k_data(num_tokens)
FN_MAP = {
"aot": aot_concat_mla_k,
"jit": jit_concat_mla_k,
"torch": torch_concat_mla_k,
}
fn = lambda: FN_MAP[provider](k, k_nope, k_rope)
quantiles = [0.5, 0.2, 0.8]
ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(fn, quantiles=quantiles)
return 1000 * ms, 1000 * max_ms, 1000 * min_ms
if IS_CI:
ABSORB_Q_VALS = list(itertools.product([4, 16], [16]))
else:
ABSORB_Q_VALS = list(itertools.product([1, 4, 8, 16, 32], [1, 8, 32, 128]))
Q_LINE_VALS = ["aot", "jit", "torch"]
Q_LINE_NAMES = ["SGL AOT Kernel", "SGL JIT Kernel", "PyTorch"]
Q_STYLES = [("orange", "-"), ("blue", "--"), ("green", "-.")]
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=["dim_0", "dim_1"],
x_vals=ABSORB_Q_VALS,
line_arg="provider",
line_vals=Q_LINE_VALS,
line_names=Q_LINE_NAMES,
styles=Q_STYLES,
ylabel="us",
plot_name="concat-mla-absorb-q-performance",
args={},
)
)
def bench_concat_mla_absorb_q(dim_0: int, dim_1: int, provider: str):
a = torch.randn(dim_0, dim_1, A_LAST_DIM, dtype=DTYPE, device=DEVICE)
b = torch.randn(dim_0, dim_1, B_LAST_DIM, dtype=DTYPE, device=DEVICE)
if provider == "torch":
out = torch.empty(
dim_0, dim_1, A_LAST_DIM + B_LAST_DIM, dtype=DTYPE, device=DEVICE
)
fn = lambda: torch_concat_mla_absorb_q(a, b, out)
else:
FN_MAP = {
"aot": aot_concat_mla_absorb_q,
"jit": jit_concat_mla_absorb_q,
}
fn = lambda: FN_MAP[provider](a, b)
quantiles = [0.5, 0.2, 0.8]
ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(fn, quantiles=quantiles)
return 1000 * ms, 1000 * max_ms, 1000 * min_ms
if __name__ == "__main__":
bench_concat_mla_k.run(print_data=True)
bench_concat_mla_absorb_q.run(print_data=True)

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python/sglang/jit_kernel/concat_mla.py Normal file
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from __future__ import annotations
from typing import TYPE_CHECKING
import torch
from sglang.jit_kernel.utils import cache_once, load_jit
if TYPE_CHECKING:
from tvm_ffi.module import Module
@cache_once
def _jit_concat_mla_k_module() -> Module:
return load_jit(
"concat_mla_k",
cuda_files=["elementwise/concat_mla.cuh"],
cuda_wrappers=[("concat_mla_k", "ConcatMlaKKernel::run")],
)
@cache_once
def _jit_concat_mla_absorb_q_module() -> Module:
return load_jit(
"concat_mla_absorb_q",
cuda_files=["elementwise/concat_mla.cuh"],
cuda_wrappers=[("concat_mla_absorb_q", "ConcatMlaAbsorbQKernel::run")],
)
def concat_mla_k(k: torch.Tensor, k_nope: torch.Tensor, k_rope: torch.Tensor) -> None:
"""
Concatenate k_nope and k_rope into k for MLA (Multi-head Latent Attention).
This kernel efficiently broadcasts k_rope across all heads while copying
k_nope values directly.
Args:
k: Output tensor of shape [num_tokens, num_heads=128, k_head_dim=192], dtype=bfloat16
k_nope: Input tensor of shape [num_tokens, num_heads=128, nope_head_dim=128], dtype=bfloat16
k_rope: Input tensor of shape [num_tokens, 1, rope_head_dim=64], dtype=bfloat16
"""
module = _jit_concat_mla_k_module()
module.concat_mla_k(k, k_nope, k_rope)
def concat_mla_absorb_q(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
"""
Concatenate tensors a and b for MLA absorbed Q computation.
Args:
a: Input tensor of shape [dim_0, dim_1, a_last_dim], dtype=bfloat16
b: Input tensor of shape [dim_0, dim_1, b_last_dim], dtype=bfloat16
Returns:
Output tensor of shape [dim_0, dim_1, a_last_dim + b_last_dim], dtype=bfloat16
"""
out = torch.empty(
(*a.shape[:-1], a.shape[-1] + b.shape[-1]),
dtype=a.dtype,
device=a.device,
)
module = _jit_concat_mla_absorb_q_module()
module.concat_mla_absorb_q(a, b, out)
return out

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python/sglang/jit_kernel/csrc/elementwise/concat_mla.cuh Normal file
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#include <sgl_kernel/tensor.h>
#include <sgl_kernel/utils.h>
#include <sgl_kernel/utils.cuh>
#include <tvm/ffi/container/tensor.h>
#include <cuda_bf16.h>
#include <cuda_runtime.h>
namespace {
// ======================= Memory Utilities =======================
// Adapted from DeepEP: https://github.com/deepseek-ai/DeepEP/blob/main/csrc/kernels/utils.cuh
SGL_DEVICE int get_lane_id() {
int lane_id;
asm("mov.s32 %0, %laneid;" : "=r"(lane_id));
return lane_id;
}
SGL_DEVICE void st_na_global_v1(const int* ptr, int v) {
asm volatile("st.global.L1::no_allocate.s32 [%0], %1;" ::"l"(ptr), "r"(v) : "memory");
}
SGL_DEVICE void st_na_global_v2(const int2* ptr, const int2& v) {
asm volatile("st.global.L1::no_allocate.v2.s32 [%0], {%1, %2};" ::"l"(ptr), "r"(v.x), "r"(v.y) : "memory");
}
SGL_DEVICE int ld_na_global_v1(const int* ptr) {
int r;
asm volatile("ld.global.nc.L1::no_allocate.s32 %0, [%1];" : "=r"(r) : "l"(ptr));
return r;
}
SGL_DEVICE int2 ld_na_global_v2(const int2* ptr) {
int2 r;
asm volatile("ld.global.nc.L1::no_allocate.v2.s32 {%0, %1}, [%2];" : "=r"(r.x), "=r"(r.y) : "l"(ptr));
return r;
}
SGL_DEVICE void prefetch_L2(const void* p) {
#if defined(ENABLE_L2_PREFETCH)
asm volatile("prefetch.global.L2 [%0];" ::"l"(p));
#endif
}
// ======================= concat_mla_k Kernel =======================
constexpr int NUM_LOCAL_HEADS = 128;
constexpr int QK_NOPE_HEAD_DIM = 128;
constexpr int QK_ROPE_HEAD_DIM = 64;
constexpr int K_HEAD_DIM = QK_NOPE_HEAD_DIM + QK_ROPE_HEAD_DIM;
constexpr int HEAD_CHUNK_SIZE = 16;
constexpr int NUM_HEAD_CHUNKS = NUM_LOCAL_HEADS / HEAD_CHUNK_SIZE;
__global__ void concat_mla_k_kernel(
bf16_t* __restrict__ k,
const bf16_t* __restrict__ k_nope,
const bf16_t* __restrict__ k_rope,
const int num_tokens,
const int64_t k_stride_0,
const int k_stride_1,
const int64_t k_nope_stride_0,
const int k_nope_stride_1,
const int64_t k_rope_stride_0) {
const int flat_warp_id = (blockIdx.x * blockDim.x + threadIdx.x) / 32;
const int token_id = flat_warp_id / NUM_HEAD_CHUNKS;
const int head_chunk_id = flat_warp_id % NUM_HEAD_CHUNKS;
const int lane_id = get_lane_id();
if (token_id >= num_tokens) return;
using NopeVec = int2; // 8B/thread, 32 threads = 256B/row
using RopeVec = int; // 4B/thread, 32 threads = 128B/row
static_assert(sizeof(NopeVec) * 32 == QK_NOPE_HEAD_DIM * sizeof(bf16_t), "nope vec mismatch");
static_assert(sizeof(RopeVec) * 32 == QK_ROPE_HEAD_DIM * sizeof(bf16_t), "rope vec mismatch");
const int head_row0 = head_chunk_id * HEAD_CHUNK_SIZE;
const int2* __restrict__ nope_src =
reinterpret_cast<const int2*>(k_nope + token_id * k_nope_stride_0 + head_row0 * k_nope_stride_1) + lane_id;
int2* __restrict__ nope_dst = reinterpret_cast<int2*>(k + token_id * k_stride_0 + head_row0 * k_stride_1) + lane_id;
int* __restrict__ rope_dst =
reinterpret_cast<int*>(k + token_id * k_stride_0 + head_row0 * k_stride_1 + QK_NOPE_HEAD_DIM) + lane_id;
const int nope_src_stride_v = (k_nope_stride_1 >> 2); // int2 covers 4 bf16
const int nope_dst_stride_v = (k_stride_1 >> 2);
const int rope_dst_stride_v = (k_stride_1 >> 1); // int covers 2 bf16
const int* rope_base = reinterpret_cast<const int*>(k_rope + token_id * k_rope_stride_0);
const RopeVec rope_val = ld_na_global_v1(rope_base + lane_id);
prefetch_L2(nope_src);
NopeVec cur = ld_na_global_v2(nope_src);
#pragma unroll
for (int i = 0; i < HEAD_CHUNK_SIZE; ++i) {
NopeVec next;
if (i + 1 < HEAD_CHUNK_SIZE) {
const int2* next_src = nope_src + nope_src_stride_v;
prefetch_L2(next_src);
next = ld_na_global_v2(next_src);
}
st_na_global_v2(nope_dst, cur);
st_na_global_v1(rope_dst, rope_val);
nope_src += nope_src_stride_v;
nope_dst += nope_dst_stride_v;
rope_dst += rope_dst_stride_v;
cur = next;
}
}
struct ConcatMlaKKernel {
static void run(tvm::ffi::TensorView k, tvm::ffi::TensorView k_nope, tvm::ffi::TensorView k_rope) {
using namespace host;
auto N = SymbolicSize{"num_tokens"};
auto H = SymbolicSize{"num_heads"};
auto D = SymbolicSize{"k_head_dim"};
auto D_nope = SymbolicSize{"nope_head_dim"};
auto D_rope = SymbolicSize{"rope_head_dim"};
auto S0_k = SymbolicSize{"k_stride_0"};
auto S1_k = SymbolicSize{"k_stride_1"};
auto S0_k_nope = SymbolicSize{"k_nope_stride_0"};
auto S1_k_nope = SymbolicSize{"k_nope_stride_1"};
auto S0_k_rope = SymbolicSize{"k_rope_stride_0"};
auto device = SymbolicDevice{};
// Set known fixed values
H.set_value(NUM_LOCAL_HEADS);
D.set_value(K_HEAD_DIM);
D_nope.set_value(QK_NOPE_HEAD_DIM);
D_rope.set_value(QK_ROPE_HEAD_DIM);
// Verify k: [num_tokens, num_heads, k_head_dim]
TensorMatcher({N, H, D}).with_strides({S0_k, S1_k, 1}).with_dtype<bf16_t>().with_device<kDLCUDA>(device).verify(k);
// Verify k_nope: [num_tokens, num_heads, nope_head_dim]
TensorMatcher({N, H, D_nope})
.with_strides({S0_k_nope, S1_k_nope, 1})
.with_dtype<bf16_t>()
.with_device<kDLCUDA>(device)
.verify(k_nope);
// Verify k_rope: [num_tokens, 1, rope_head_dim]
TensorMatcher({N, 1, D_rope})
.with_strides({S0_k_rope, -1, 1})
.with_dtype<bf16_t>()
.with_device<kDLCUDA>(device)
.verify(k_rope);
// Check alignment
RuntimeCheck(reinterpret_cast<uintptr_t>(k.data_ptr()) % 16 == 0, "Tensor k must be 16-byte aligned");
RuntimeCheck(reinterpret_cast<uintptr_t>(k_nope.data_ptr()) % 16 == 0, "Tensor k_nope must be 16-byte aligned");
RuntimeCheck(reinterpret_cast<uintptr_t>(k_rope.data_ptr()) % 16 == 0, "Tensor k_rope must be 16-byte aligned");
const int num_tokens = static_cast<int>(N.unwrap());
constexpr int num_warps_per_block = 32;
const int grid_size = div_ceil(num_tokens * NUM_HEAD_CHUNKS, num_warps_per_block);
const int block_size = num_warps_per_block * 32;
LaunchKernel(grid_size, block_size, device.unwrap())(
concat_mla_k_kernel,
static_cast<bf16_t*>(k.data_ptr()),
static_cast<const bf16_t*>(k_nope.data_ptr()),
static_cast<const bf16_t*>(k_rope.data_ptr()),
num_tokens,
S0_k.unwrap(),
static_cast<int>(S1_k.unwrap()),
S0_k_nope.unwrap(),
static_cast<int>(S1_k_nope.unwrap()),
S0_k_rope.unwrap());
}
};
// ======================= concat_mla_absorb_q Kernel =======================
constexpr int A_LAST_DIM = 512;
constexpr int B_LAST_DIM = 64;
constexpr int OUT_LAST_DIM = A_LAST_DIM + B_LAST_DIM;
__global__ void concat_mla_absorb_q_kernel(
bf16_t* a,
bf16_t* b,
bf16_t* out,
const int num_items,
const int dim_1,
const int64_t a_stride_0,
const int a_stride_1,
const int64_t b_stride_0,
const int b_stride_1,
const int64_t out_stride_0,
const int out_stride_1) {
const int flat_warp_id = (blockIdx.x * blockDim.x + threadIdx.x) / 32;
const int lane_id = get_lane_id();
const int idx_0 = flat_warp_id / dim_1;
const int idx_1 = flat_warp_id % dim_1;
if (flat_warp_id >= num_items) {
return;
}
using ABufType = int4;
constexpr int A_NUM_UNROLL = 2;
static_assert(sizeof(ABufType) * A_NUM_UNROLL == A_LAST_DIM * sizeof(a[0]) / 32);
ABufType a_buf[A_NUM_UNROLL];
using BBufType = int;
constexpr int B_NUM_UNROLL = 1;
static_assert(sizeof(BBufType) * B_NUM_UNROLL == B_LAST_DIM * sizeof(b[0]) / 32);
BBufType b_buf;
{
const BBufType* base_addr = reinterpret_cast<BBufType*>(b + idx_0 * b_stride_0 + idx_1 * b_stride_1);
b_buf = *(base_addr + lane_id);
}
#pragma unroll
for (int i = 0; i < A_NUM_UNROLL; ++i) {
const ABufType* base_addr = reinterpret_cast<ABufType*>(a + idx_0 * a_stride_0 + idx_1 * a_stride_1);
a_buf[i] = *(base_addr + i * 32 + lane_id);
}
{
BBufType* base_addr = reinterpret_cast<BBufType*>(out + idx_0 * out_stride_0 + idx_1 * out_stride_1 + A_LAST_DIM);
*(base_addr + lane_id) = b_buf;
}
#pragma unroll
for (int i = 0; i < A_NUM_UNROLL; ++i) {
ABufType* base_addr = reinterpret_cast<ABufType*>(out + idx_0 * out_stride_0 + idx_1 * out_stride_1);
*(base_addr + i * 32 + lane_id) = a_buf[i];
}
}
struct ConcatMlaAbsorbQKernel {
static void run(tvm::ffi::TensorView a, tvm::ffi::TensorView b, tvm::ffi::TensorView out) {
using namespace host;
auto N0_a = SymbolicSize{"a_dim_0"};
auto N1_a = SymbolicSize{"a_dim_1"};
auto D_a = SymbolicSize{"a_last_dim"};
auto N0_b = SymbolicSize{"b_dim_0"};
auto N1_b = SymbolicSize{"b_dim_1"};
auto D_b = SymbolicSize{"b_last_dim"};
auto N0_out = SymbolicSize{"out_dim_0"};
auto N1_out = SymbolicSize{"out_dim_1"};
auto D_out = SymbolicSize{"out_last_dim"};
auto S0_a = SymbolicSize{"a_stride_0"};
auto S1_a = SymbolicSize{"a_stride_1"};
auto S0_b = SymbolicSize{"b_stride_0"};
auto S1_b = SymbolicSize{"b_stride_1"};
auto S0_out = SymbolicSize{"out_stride_0"};
auto S1_out = SymbolicSize{"out_stride_1"};
auto device = SymbolicDevice{};
// Set known fixed values
D_a.set_value(A_LAST_DIM);
D_b.set_value(B_LAST_DIM);
D_out.set_value(OUT_LAST_DIM);
// Verify a: [dim_0, dim_1, A_LAST_DIM]
TensorMatcher({N0_a, N1_a, D_a})
.with_strides({S0_a, S1_a, 1})
.with_dtype<bf16_t>()
.with_device<kDLCUDA>(device)
.verify(a);
// Verify b: [dim_0, dim_1, B_LAST_DIM]
TensorMatcher({N0_b, N1_b, D_b})
.with_strides({S0_b, S1_b, 1})
.with_dtype<bf16_t>()
.with_device<kDLCUDA>(device)
.verify(b);
// Verify out: [dim_0, dim_1, OUT_LAST_DIM]
TensorMatcher({N0_out, N1_out, D_out})
.with_strides({S0_out, S1_out, 1})
.with_dtype<bf16_t>()
.with_device<kDLCUDA>(device)
.verify(out);
// Check alignment
RuntimeCheck(reinterpret_cast<uintptr_t>(a.data_ptr()) % 16 == 0, "Tensor a must be 16-byte aligned");
RuntimeCheck(reinterpret_cast<uintptr_t>(b.data_ptr()) % 16 == 0, "Tensor b must be 16-byte aligned");
RuntimeCheck(reinterpret_cast<uintptr_t>(out.data_ptr()) % 16 == 0, "Tensor out must be 16-byte aligned");
// Verify dimensions match: a.size(0) * a.size(1) == b.size(0) * b.size(1)
RuntimeCheck(
N0_a.unwrap() * N1_a.unwrap() == N0_b.unwrap() * N1_b.unwrap(),
"Dimension mismatch: a.size(0) * a.size(1) must equal b.size(0) * b.size(1)");
RuntimeCheck(N1_a.unwrap() == N1_b.unwrap(), "Dimension mismatch: a.size(1) must equal b.size(1)");
const int num_items = static_cast<int>(N0_a.unwrap() * N1_a.unwrap());
const int dim_1 = static_cast<int>(N1_a.unwrap());
constexpr int num_warps_per_block = 32;
const int grid_size = div_ceil(num_items, num_warps_per_block);
const int block_size = num_warps_per_block * 32;
LaunchKernel(grid_size, block_size, device.unwrap())(
concat_mla_absorb_q_kernel,
static_cast<bf16_t*>(a.data_ptr()),
static_cast<bf16_t*>(b.data_ptr()),
static_cast<bf16_t*>(out.data_ptr()),
num_items,
dim_1,
S0_a.unwrap(),
static_cast<int>(S1_a.unwrap()),
S0_b.unwrap(),
static_cast<int>(S1_b.unwrap()),
S0_out.unwrap(),
static_cast<int>(S1_out.unwrap()));
}
};
} // namespace

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python/sglang/jit_kernel/tests/test_concat_mla.py Normal file
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import itertools
import pytest
import torch
import triton
def torch_concat_mla_k(
k: torch.Tensor, k_nope: torch.Tensor, k_rope: torch.Tensor
) -> None:
"""Reference PyTorch implementation for concat_mla_k."""
# k_nope: [num_tokens, num_heads, nope_head_dim]
# k_rope: [num_tokens, 1, rope_head_dim]
# k: [num_tokens, num_heads, nope_head_dim + rope_head_dim]
nope_head_dim = k_nope.shape[-1]
k[:, :, :nope_head_dim] = k_nope
# Broadcast k_rope across all heads
k[:, :, nope_head_dim:] = k_rope.expand(-1, k.shape[1], -1)
def torch_concat_mla_absorb_q(
a: torch.Tensor, b: torch.Tensor, out: torch.Tensor
) -> None:
"""Reference PyTorch implementation for concat_mla_absorb_q."""
# a: [dim_0, dim_1, a_last_dim]
# b: [dim_0, dim_1, b_last_dim]
# out: [dim_0, dim_1, a_last_dim + b_last_dim]
a_last_dim = a.shape[-1]
out[:, :, :a_last_dim] = a
out[:, :, a_last_dim:] = b
def sgl_kernel_concat_mla_k(
k: torch.Tensor, k_nope: torch.Tensor, k_rope: torch.Tensor
) -> None:
"""AOT compiled sgl_kernel implementation."""
from sgl_kernel import concat_mla_k
concat_mla_k(k, k_nope, k_rope)
def sgl_kernel_concat_mla_absorb_q(
a: torch.Tensor, b: torch.Tensor, out: torch.Tensor
) -> None:
"""AOT compiled sgl_kernel implementation."""
from sgl_kernel import concat_mla_absorb_q
result = concat_mla_absorb_q(a, b) # AOT returns output
out.copy_(result) # Copy to provided tensor for comparison
def jit_concat_mla_k(
k: torch.Tensor, k_nope: torch.Tensor, k_rope: torch.Tensor
) -> None:
"""JIT compiled implementation."""
from sglang.jit_kernel.concat_mla import concat_mla_k
concat_mla_k(k, k_nope, k_rope)
def jit_concat_mla_absorb_q(
a: torch.Tensor, b: torch.Tensor, out: torch.Tensor
) -> None:
"""JIT compiled implementation - wrapper for test compatibility."""
from sglang.jit_kernel.concat_mla import concat_mla_absorb_q
result = concat_mla_absorb_q(a, b)
out.copy_(result)
# Constants matching the kernel
NUM_LOCAL_HEADS = 128
QK_NOPE_HEAD_DIM = 128
QK_ROPE_HEAD_DIM = 64
K_HEAD_DIM = QK_NOPE_HEAD_DIM + QK_ROPE_HEAD_DIM
A_LAST_DIM = 512
B_LAST_DIM = 64
OUT_LAST_DIM = A_LAST_DIM + B_LAST_DIM
DEVICE = "cuda"
DTYPE = torch.bfloat16
# Test configurations
NUM_TOKENS_LIST = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
@pytest.mark.parametrize("num_tokens", NUM_TOKENS_LIST)
def test_concat_mla_k_jit_vs_torch(num_tokens: int) -> None:
"""Test JIT kernel against PyTorch reference."""
k_jit = torch.empty(
num_tokens, NUM_LOCAL_HEADS, K_HEAD_DIM, device=DEVICE, dtype=DTYPE
)
k_torch = torch.empty(
num_tokens, NUM_LOCAL_HEADS, K_HEAD_DIM, device=DEVICE, dtype=DTYPE
)
k_nope = torch.randn(
num_tokens, NUM_LOCAL_HEADS, QK_NOPE_HEAD_DIM, device=DEVICE, dtype=DTYPE
)
k_rope = torch.randn(num_tokens, 1, QK_ROPE_HEAD_DIM, device=DEVICE, dtype=DTYPE)
torch_concat_mla_k(k_torch, k_nope, k_rope)
jit_concat_mla_k(k_jit, k_nope, k_rope)
triton.testing.assert_close(k_jit, k_torch, atol=0, rtol=0)
@pytest.mark.parametrize("num_tokens", NUM_TOKENS_LIST)
def test_concat_mla_k_jit_vs_aot(num_tokens: int) -> None:
"""Test JIT kernel against AOT kernel for bitwise equivalence."""
k_jit = torch.empty(
num_tokens, NUM_LOCAL_HEADS, K_HEAD_DIM, device=DEVICE, dtype=DTYPE
)
k_aot = torch.empty(
num_tokens, NUM_LOCAL_HEADS, K_HEAD_DIM, device=DEVICE, dtype=DTYPE
)
k_nope = torch.randn(
num_tokens, NUM_LOCAL_HEADS, QK_NOPE_HEAD_DIM, device=DEVICE, dtype=DTYPE
)
k_rope = torch.randn(num_tokens, 1, QK_ROPE_HEAD_DIM, device=DEVICE, dtype=DTYPE)
sgl_kernel_concat_mla_k(k_aot, k_nope, k_rope)
jit_concat_mla_k(k_jit, k_nope, k_rope)
triton.testing.assert_close(k_jit, k_aot, atol=0, rtol=0)
DIM_0_LIST = [1, 2, 4, 8, 16, 32]
DIM_1_LIST = [1, 2, 4, 8, 16, 128]
@pytest.mark.parametrize(
"dim_0,dim_1",
list(itertools.product(DIM_0_LIST, DIM_1_LIST)),
)
def test_concat_mla_absorb_q_jit_vs_torch(dim_0: int, dim_1: int) -> None:
"""Test JIT kernel against PyTorch reference."""
a = torch.randn(dim_0, dim_1, A_LAST_DIM, device=DEVICE, dtype=DTYPE)
b = torch.randn(dim_0, dim_1, B_LAST_DIM, device=DEVICE, dtype=DTYPE)
out_jit = torch.empty(dim_0, dim_1, OUT_LAST_DIM, device=DEVICE, dtype=DTYPE)
out_torch = torch.empty(dim_0, dim_1, OUT_LAST_DIM, device=DEVICE, dtype=DTYPE)
torch_concat_mla_absorb_q(a, b, out_torch)
jit_concat_mla_absorb_q(a, b, out_jit)
triton.testing.assert_close(out_jit, out_torch, atol=0, rtol=0)
@pytest.mark.parametrize(
"dim_0,dim_1",
list(itertools.product(DIM_0_LIST, DIM_1_LIST)),
)
def test_concat_mla_absorb_q_jit_vs_aot(dim_0: int, dim_1: int) -> None:
"""Test JIT kernel against AOT kernel for bitwise equivalence."""
a = torch.randn(dim_0, dim_1, A_LAST_DIM, device=DEVICE, dtype=DTYPE)
b = torch.randn(dim_0, dim_1, B_LAST_DIM, device=DEVICE, dtype=DTYPE)
out_jit = torch.empty(dim_0, dim_1, OUT_LAST_DIM, device=DEVICE, dtype=DTYPE)
out_aot = torch.empty(dim_0, dim_1, OUT_LAST_DIM, device=DEVICE, dtype=DTYPE)
sgl_kernel_concat_mla_absorb_q(a, b, out_aot)
jit_concat_mla_absorb_q(a, b, out_jit)
triton.testing.assert_close(out_jit, out_aot, atol=0, rtol=0)
if __name__ == "__main__":
pytest.main([__file__])