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[fix] fix cutlass_mla_backend with cuda_graph and add sm_scale for sgl-kernel cutlass_mla (#7184)

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This commit is contained in:
JieXin Liang
2025-06-15 03:45:41 +08:00
committed by GitHub
parent ed54bf9d19
commit ab1a4fa5cb
7 changed files with 29 additions and 17 deletions
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5
python/sglang/srt/layers/attention/cutlass_mla_backend.py
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@@ -108,7 +108,7 @@ class CutlassMLABackend(FlashInferMLAAttnBackend):
PAGE_SIZE,
)
workspace_size = cutlass_mla_get_workspace_size(
max_seqlen_pad * PAGE_SIZE, bs
max_seqlen_pad * PAGE_SIZE, bs, num_kv_splits=1
)
workspace = torch.empty(
workspace_size, device="cuda", dtype=torch.uint8
@@ -138,7 +138,7 @@ class CutlassMLABackend(FlashInferMLAAttnBackend):
cuda_graph_kv_indices = block_kv_indices
workspace_size = cutlass_mla_get_workspace_size(
cuda_graph_kv_indices.shape[1] * PAGE_SIZE, max_bs
cuda_graph_kv_indices.shape[1] * PAGE_SIZE, max_bs, num_kv_splits=1
)
self.cuda_graph_mla_workspace = torch.empty(
workspace_size, device="cuda", dtype=torch.uint8
@@ -280,6 +280,7 @@ class CutlassMLABackend(FlashInferMLAAttnBackend):
seq_lens=forward_batch.seq_lens.to(torch.int32),
page_table=self.forward_metadata.block_kv_indices,
workspace=self.forward_metadata.workspace,
num_kv_splits=1,
)
return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)

1
sgl-kernel/benchmark/bench_cutlass_mla.py
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@@ -95,6 +95,7 @@ def benchmark(batch_size, seq_len, provider, block_size, num_kv_splits):
seq_lens,
block_table,
workspace,
1.44,
num_kv_splits,
),
quantiles=quantiles,

19
sgl-kernel/csrc/attention/cutlass_mla_kernel.cu
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@@ -36,7 +36,8 @@ void cutlass_mla_decode(
torch::Tensor const& kv_c_and_k_pe_cache,
torch::Tensor const& seq_lens,
torch::Tensor const& page_table,
torch::Tensor const& workspace) {
torch::Tensor const& workspace,
int64_t num_kv_splits) {
TORCH_CHECK(false, "CUDA version must be >= 12.4 for cutlass_mla_decode");
}
int64_t cutlass_mla_get_workspace_size(int64_t max_seq_len, int64_t num_batches, int64_t sm_count, int64_t num_kv_splits) {
@@ -98,6 +99,7 @@ typename T::Fmha::Arguments args_from_options(
at::Tensor const& kv_c_and_k_pe_cache,
at::Tensor const& seq_lens,
at::Tensor const& page_table,
double sm_scale,
int64_t num_kv_splits) {
cutlass::KernelHardwareInfo hw_info;
hw_info.device_id = q_nope.device().index();
@@ -115,10 +117,7 @@ typename T::Fmha::Arguments args_from_options(
auto [H, K, D, B] = problem_shape;
auto [D_latent, D_rope] = D;
// the scale is based on the non-absorbed sizes, change as appropriate
// we can't determine this parameter from the info we have, it's an input
int D_non_latent = 128;
float scale = 1.0 / sqrt(1.0 * (D_non_latent + D_rope));
float scale = float(sm_scale);
using StrideQ = typename T::StrideQ;
using StrideK = typename T::StrideK;
@@ -183,11 +182,12 @@ void runMla(
at::Tensor const& seq_lens,
at::Tensor const& page_table,
at::Tensor const& workspace,
double sm_scale,
int64_t num_kv_splits,
cudaStream_t stream) {
using MlaSm100Type = MlaSm100<Element, IsPaged128, PersistenceOption>;
typename MlaSm100Type::Fmha fmha;
auto arguments = args_from_options<MlaSm100Type>(out, q_nope, 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, sm_scale, num_kv_splits);
CUTLASS_CHECK(fmha.can_implement(arguments));
@@ -215,6 +215,7 @@ void cutlass_mla_decode(
torch::Tensor const& seq_lens,
torch::Tensor const& page_table,
torch::Tensor const& workspace,
double sm_scale,
int64_t num_kv_splits) {
auto in_dtype = q_nope.dtype();
at::cuda::CUDAGuard device_guard{(char)q_nope.get_device()};
@@ -228,13 +229,13 @@ 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, 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, sm_scale, num_kv_splits, stream);
} else if (in_dtype == at::ScalarType::BFloat16) {
runMla<cutlass::bfloat16_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
out, q_nope, 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, sm_scale, num_kv_splits, stream);
} else if (in_dtype == at::ScalarType::Float8_e4m3fn) {
runMla<cutlass::float_e4m3_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
out, q_nope, 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, sm_scale, num_kv_splits, stream);
} else {
TORCH_CHECK(false, "Unsupported input data type of MLA");
}

2
sgl-kernel/csrc/common_extension.cc
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@@ -60,7 +60,7 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
m.impl("merge_state_v2", torch::kCUDA, &merge_state_v2);
m.def(
"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) -> ()");
"page_table, Tensor! workspace, float sm_scale, 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);

8
sgl-kernel/include/sgl_kernel_ops.h Executable file → Normal file
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@@ -111,9 +111,13 @@ void cutlass_mla_decode(
torch::Tensor const& seq_lens,
torch::Tensor const& page_table,
torch::Tensor const& workspace,
int64_t num_kv_splits = -1);
double sm_scale,
int64_t num_kv_splits = 1 /* Set to 1 to avoid cuda_graph issue by default. */);
int64_t cutlass_mla_get_workspace_size(
int64_t max_seq_len, int64_t num_batches, int64_t sm_count = 0, int64_t num_kv_splits = -1);
int64_t max_seq_len,
int64_t num_batches,
int64_t sm_count = 0,
int64_t num_kv_splits = 1 /* Set to 1 to avoid cuda_graph issue by default. */);
/*
* From csrc/elementwise
*/

9
sgl-kernel/python/sgl_kernel/attention.py
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@@ -58,7 +58,8 @@ def cutlass_mla_decode(
seq_lens: torch.Tensor,
page_table: torch.Tensor,
workspace: torch.Tensor,
num_kv_splits: int = -1,
sm_scale: float,
num_kv_splits: int = 1, # Set to 1 to avoid cuda_graph issue by default.
) -> torch.Tensor:
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}"
@@ -118,13 +119,17 @@ def cutlass_mla_decode(
seq_lens,
page_table,
workspace,
sm_scale,
num_kv_splits,
)
return out[:, :H].contiguous()
def cutlass_mla_get_workspace_size(
max_seq_len: int, num_batches: int, sm_count: int = 0, num_kv_splits: int = -1
max_seq_len: int,
num_batches: int,
sm_count: int = 0,
num_kv_splits: int = 1, # Set to 1 to avoid cuda_graph issue by default.
) -> int:
assert max_seq_len > 0, f"max_seq_len must be greater than 0, got {max_seq_len}"
assert num_batches > 0, f"num_batches must be greater than 0, got {num_batches}"

2
sgl-kernel/tests/test_cutlass_mla.py
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@@ -93,7 +93,7 @@ def test_cutlass_mla_decode(
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_nope, q_pe, kv_cache, seq_lens, block_table, workspace, num_kv_splits
q_nope, q_pe, kv_cache, seq_lens, block_table, workspace, scale, num_kv_splits
)
torch.testing.assert_close(out, out_ref, atol=1e-2, rtol=1e-2)