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

Remove 200us slow concat kernel (part 1: kernel) (#7145)

Browse Source
This commit is contained in:
fzyzcjy
2025-06-13 16:58:29 +08:00
committed by GitHub
parent 2f4ec752bc
commit aa46ed34d2
6 changed files with 79 additions and 48 deletions
Download Patch File Download Diff File Expand all files Collapse all files

21
sgl-kernel/benchmark/bench_cutlass_mla.py
View File

@@ -38,6 +38,7 @@ configs = list(itertools.product(bs_range, qlen_range))
)
def benchmark(batch_size, seq_len, provider, block_size, num_kv_splits):
d = 576
dn = 64
dv = 512
h_q_map = {
@@ -63,7 +64,11 @@ def benchmark(batch_size, seq_len, provider, block_size, num_kv_splits):
pack_factor = 128 // block_size
block_num = ((block_num + pack_factor - 1) // pack_factor) * pack_factor
q = torch.randn(batch_size, h_q, d, dtype=torch.bfloat16, device="cuda") * 100.0
qn = (
torch.randn(h_q, batch_size, d - dn, dtype=torch.bfloat16, device="cuda")
* 100.0
)
qr = torch.randn(batch_size, h_q, dn, dtype=torch.bfloat16, device="cuda") * 100.0
block_table = torch.randint(
0,
batch_size * block_num,
@@ -84,16 +89,22 @@ def benchmark(batch_size, seq_len, provider, block_size, num_kv_splits):
quantiles = [0.5, 0.2, 0.8]
ms, min_ms, max_ms = triton.testing.do_bench(
lambda: cutlass_mla_decode(
q, kv_cache, seq_lens, block_table, workspace, num_kv_splits
qn.transpose(0, 1),
qr,
kv_cache,
seq_lens,
block_table,
workspace,
num_kv_splits,
),
quantiles=quantiles,
)
q_size = qn.numel() * qn.element_size() + qr.numel() * qr.element_size()
gbps = (
lambda ms: (
q.numel() * q.element_size()
+ q.numel() * q.element_size() * dv / d
+ kv_cache.numel() * kv_cache.element_size()
q_size + q_size * dv / d + kv_cache.numel() * kv_cache.element_size()
)
* 1e-9
/ (ms * 1e-3)

49
sgl-kernel/csrc/attention/cutlass_mla_kernel.cu
View File

@@ -22,6 +22,7 @@ limitations under the License.
#include <torch/all.h>
#include <cute/tensor.hpp>
#include <iostream>
#include "cutlass_sm100_mla/device/sm100_mla.hpp"
#include "cutlass_sm100_mla/kernel/sm100_mla_tile_scheduler.hpp"
@@ -30,7 +31,8 @@ limitations under the License.
#if !defined(CUDA_VERSION) || CUDA_VERSION < 12040
void cutlass_mla_decode(
torch::Tensor const& out,
torch::Tensor const& q_nope_and_q_pe,
torch::Tensor const& q_nope,
torch::Tensor const& q_pe,
torch::Tensor const& kv_c_and_k_pe_cache,
torch::Tensor const& seq_lens,
torch::Tensor const& page_table,
@@ -91,16 +93,17 @@ struct MlaSm100 {
template <typename T>
typename T::Fmha::Arguments args_from_options(
at::Tensor const& out,
at::Tensor const& q_nope_and_q_pe,
at::Tensor const& q_nope,
at::Tensor const& q_pe,
at::Tensor const& kv_c_and_k_pe_cache,
at::Tensor const& seq_lens,
at::Tensor const& page_table,
int64_t num_kv_splits) {
cutlass::KernelHardwareInfo hw_info;
hw_info.device_id = q_nope_and_q_pe.device().index();
hw_info.device_id = q_nope.device().index();
hw_info.sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
int batches = q_nope_and_q_pe.sizes()[0];
int batches = q_nope.sizes()[0];
int page_count_per_seq = page_table.sizes()[1];
int page_count_total = kv_c_and_k_pe_cache.sizes()[0];
int page_size = kv_c_and_k_pe_cache.sizes()[1];
@@ -122,8 +125,11 @@ typename T::Fmha::Arguments args_from_options(
using StrideO = typename T::StrideO;
using StrideLSE = typename T::StrideLSE;
StrideQ stride_Q = cute::make_tuple(
static_cast<int64_t>(0 + D_latent + D_rope), _1{}, static_cast<int64_t>(H * (0 + D_latent + D_rope)));
StrideQ stride_Q_nope = cute::make_tuple(
static_cast<int64_t>(q_nope.stride(1)), _1{}, static_cast<int64_t>(q_nope.stride(0)));
StrideQ stride_Q_pe = cute::make_tuple(
static_cast<int64_t>(q_pe.stride(1)), _1{}, static_cast<int64_t>(q_pe.stride(0)));
StrideK stride_C = cute::make_tuple(
static_cast<int64_t>(0 + D_latent + D_rope), _1{}, static_cast<int64_t>(page_size * (D_latent + D_rope)));
StrideLSE stride_PT = cute::make_stride(_1{}, page_count_per_seq);
@@ -133,15 +139,16 @@ typename T::Fmha::Arguments args_from_options(
using Element = typename T::Element;
using ElementOut = typename T::ElementOut;
using ElementAcc = typename T::ElementAcc;
auto Q_ptr = static_cast<Element*>(q_nope_and_q_pe.data_ptr());
auto Q_nope_ptr = static_cast<Element*>(q_nope.data_ptr());
auto Q_pe_ptr = static_cast<Element*>(q_pe.data_ptr());
auto C_ptr = static_cast<Element*>(kv_c_and_k_pe_cache.data_ptr());
typename T::Fmha::Arguments arguments{
problem_shape,
{scale,
Q_ptr,
stride_Q,
Q_ptr + D_latent,
stride_Q,
Q_nope_ptr,
stride_Q_nope,
Q_pe_ptr,
stride_Q_pe,
C_ptr,
stride_C,
C_ptr + D_latent,
@@ -170,7 +177,8 @@ typename T::Fmha::Arguments args_from_options(
template <typename Element, bool IsPaged128, typename PersistenceOption>
void runMla(
at::Tensor const& out,
at::Tensor const& q_nope_and_q_pe,
at::Tensor const& q_nope,
at::Tensor const& q_pe,
at::Tensor const& kv_c_and_k_pe_cache,
at::Tensor const& seq_lens,
at::Tensor const& page_table,
@@ -179,7 +187,7 @@ void runMla(
cudaStream_t stream) {
using MlaSm100Type = MlaSm100<Element, IsPaged128, PersistenceOption>;
typename MlaSm100Type::Fmha fmha;
auto arguments = args_from_options<MlaSm100Type>(out, q_nope_and_q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, num_kv_splits);
auto arguments = args_from_options<MlaSm100Type>(out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, num_kv_splits);
CUTLASS_CHECK(fmha.can_implement(arguments));
@@ -201,15 +209,16 @@ void runMla(
void cutlass_mla_decode(
torch::Tensor const& out,
torch::Tensor const& q_nope_and_q_pe,
torch::Tensor const& q_nope,
torch::Tensor const& q_pe,
torch::Tensor const& kv_c_and_k_pe_cache,
torch::Tensor const& seq_lens,
torch::Tensor const& page_table,
torch::Tensor const& workspace,
int64_t num_kv_splits) {
auto in_dtype = q_nope_and_q_pe.dtype();
at::cuda::CUDAGuard device_guard{(char)q_nope_and_q_pe.get_device()};
const cudaStream_t stream = at::cuda::getCurrentCUDAStream(q_nope_and_q_pe.get_device());
auto in_dtype = q_nope.dtype();
at::cuda::CUDAGuard device_guard{(char)q_nope.get_device()};
const cudaStream_t stream = at::cuda::getCurrentCUDAStream(q_nope.get_device());
const int page_size = kv_c_and_k_pe_cache.sizes()[1];
// NOTE(alcanderian): IsPersistent has bug with manual split_kv.
@@ -219,13 +228,13 @@ void cutlass_mla_decode(
DISPATCH_BOOL(num_kv_splits <= 1, NotManualSplitKV, [&] {
if (in_dtype == at::ScalarType::Half) {
runMla<cutlass::half_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
out, q_nope_and_q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, num_kv_splits, stream);
out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, num_kv_splits, stream);
} else if (in_dtype == at::ScalarType::BFloat16) {
runMla<cutlass::bfloat16_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
out, q_nope_and_q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, num_kv_splits, stream);
out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, num_kv_splits, stream);
} else if (in_dtype == at::ScalarType::Float8_e4m3fn) {
runMla<cutlass::float_e4m3_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
out, q_nope_and_q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, num_kv_splits, stream);
out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, num_kv_splits, stream);
} else {
TORCH_CHECK(false, "Unsupported input data type of MLA");
}

2
sgl-kernel/csrc/common_extension.cc
View File

@@ -59,7 +59,7 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
m.def("merge_state_v2(Tensor v_a, Tensor s_a, Tensor v_b, Tensor s_b, Tensor! v_merged, Tensor! s_merged) -> ()");
m.impl("merge_state_v2", torch::kCUDA, &merge_state_v2);
m.def(
"cutlass_mla_decode(Tensor! out, Tensor q_nope_and_q_pe, Tensor kv_c_and_k_pe_cache, Tensor seq_lens, Tensor "
"cutlass_mla_decode(Tensor! out, Tensor q_nope, Tensor q_pe, Tensor kv_c_and_k_pe_cache, Tensor seq_lens, Tensor "
"page_table, Tensor! workspace, int num_kv_splits) -> ()");
m.impl("cutlass_mla_decode", torch::kCUDA, &cutlass_mla_decode);
m.def("cutlass_mla_get_workspace_size", &cutlass_mla_get_workspace_size);

3
sgl-kernel/include/sgl_kernel_ops.h
View File

@@ -105,7 +105,8 @@ void merge_state_v2(
at::Tensor v_a, at::Tensor s_a, at::Tensor v_b, at::Tensor s_b, at::Tensor v_merged, at::Tensor s_merged);
void cutlass_mla_decode(
torch::Tensor const& out,
torch::Tensor const& q_nope_and_q_pe,
torch::Tensor const& q_nope,
torch::Tensor const& q_pe,
torch::Tensor const& kv_c_and_k_pe_cache,
torch::Tensor const& seq_lens,
torch::Tensor const& page_table,

46
sgl-kernel/python/sgl_kernel/attention.py
View File

@@ -52,34 +52,42 @@ def merge_state_v2(
def cutlass_mla_decode(
q_nope_and_q_pe: torch.Tensor,
q_nope: torch.Tensor,
q_pe: torch.Tensor,
kv_c_and_k_pe_cache: torch.Tensor,
seq_lens: torch.Tensor,
page_table: torch.Tensor,
workspace: torch.Tensor,
num_kv_splits: int = -1,
) -> torch.Tensor:
assert (
q_nope_and_q_pe.ndim == 3
), f"q_nope_and_q_pe must be a 3D tensor, but got {q_nope_and_q_pe.ndim}"
assert q_nope.ndim == 3, f"q_nope must be a 3D tensor, but got {q_nope.ndim}"
assert q_pe.ndim == 3, f"q_pe must be a 3D tensor, but got {q_pe.ndim}"
assert (
kv_c_and_k_pe_cache.ndim == 3
), f"kv_c_and_k_pe_cache must be a 3D tensor, but got {kv_c_and_k_pe_cache.ndim}"
B_q, H, D_q = q_nope_and_q_pe.shape
B_q, H, D_q_nope = q_nope.shape
B_q_2, H_2, D_q_pe = q_pe.shape
assert (B_q == B_q_2) and (H == H_2)
_, PAGE_SIZE, D_ckv = kv_c_and_k_pe_cache.shape
D_latent = 512
D_rope = 64
assert D_q == D_ckv and D_q == D_latent + D_rope, (
f"D_q must be equal to D_ckv and D_q must be equal to D_latent + D_rope, "
f"but got D_q = {D_q}, D_ckv = {D_ckv}, D_latent = {D_latent}, D_rope = {D_rope}"
)
assert D_q_nope == D_latent
assert D_q_pe == D_rope
assert D_ckv == D_latent + D_rope
MAX_HEADS = 128
assert H <= MAX_HEADS, f"H must be <= {MAX_HEADS}, but got {H}"
if H < MAX_HEADS:
q_nope_and_q_pe_padded = q_nope_and_q_pe.new_empty((B_q, MAX_HEADS, D_q))
q_nope_and_q_pe_padded[:, :H] = q_nope_and_q_pe
q_nope_and_q_pe = q_nope_and_q_pe_padded
q_nope_padded = q_nope.new_empty((B_q, MAX_HEADS, D_q_nope))
q_nope_padded[:, :H] = q_nope
q_nope = q_nope_padded
q_pe_padded = q_pe.new_empty((B_q, MAX_HEADS, D_q_pe))
q_pe_padded[:, :H] = q_pe
q_pe = q_pe_padded
assert len(page_table.shape) == 2
B_block_table, block_num = page_table.shape
@@ -88,14 +96,11 @@ def cutlass_mla_decode(
assert block_num % (128 / PAGE_SIZE) == 0
# TODO(kaixih@nvidia): support fp8
assert q_nope_and_q_pe.dtype in (
assert q_nope.dtype in (
torch.float16,
torch.bfloat16,
), f"q_nope_and_q_pe.dtype needs to be fp16 or bf16 but got {q_nope_and_q_pe.dtype}."
assert kv_c_and_k_pe_cache.dtype == q_nope_and_q_pe.dtype, (
f"kv_c_and_k_pe_cache.dtype needs to be the same as q_nope_and_q_pe.dtype, "
f"but got {kv_c_and_k_pe_cache.dtype}."
)
), f"q_nope.dtype needs to be fp16 or bf16 but got {q_nope.dtype}."
assert q_nope.dtype == q_pe.dtype == kv_c_and_k_pe_cache.dtype
assert (
seq_lens.dtype == torch.int32
), f"seq_lens.dtype needs to be int32 but got {seq_lens.dtype}."
@@ -103,11 +108,12 @@ def cutlass_mla_decode(
page_table.dtype == torch.int32
), f"page_table.dtype needs to be int32 but got {page_table.dtype}."
out = q_nope_and_q_pe.new_empty((B_q, MAX_HEADS, D_latent))
out = q_nope.new_empty((B_q, MAX_HEADS, D_latent))
torch.ops.sgl_kernel.cutlass_mla_decode.default(
out,
q_nope_and_q_pe,
q_nope,
q_pe,
kv_c_and_k_pe_cache,
seq_lens,
page_table,

6
sgl-kernel/tests/test_cutlass_mla.py
View File

@@ -86,10 +86,14 @@ def test_cutlass_mla_decode(
)
workspace = torch.empty(workspace_size, device="cuda", dtype=torch.uint8)
q_nope = torch.empty((h_q, bs, dv)).transpose(0, 1)
q_nope.copy_(q[:, :, :dv])
q_pe = q[:, :, dv:].clone()
out_ref = q.new_zeros(bs, h_q, dv)
ref_mla(out_ref, q, kv_cache, scale, block_table, seq_lens)
out = cutlass_mla_decode(
q, kv_cache, seq_lens, block_table, workspace, num_kv_splits
q_nope, q_pe, kv_cache, seq_lens, block_table, workspace, num_kv_splits
)
torch.testing.assert_close(out, out_ref, atol=1e-2, rtol=1e-2)