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kernel: use 8 warps to avoid register spilling for mla with hdim=512
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@guocuimi
guocuimi committed Feb 13, 2025
1 parent 66664a8 commit 9463046
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Showing 6 changed files with 132 additions and 63 deletions.
94 changes: 66 additions & 28 deletions src/kernels/attention/mla_kernel_sm80.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -45,11 +45,13 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(
// type alias
using DType = typename Traits::DType;

using TiledMma = typename Traits::TiledMma;
using TiledMma_QK = typename Traits::TiledMma_QK;
using TiledMma_PV = typename Traits::TiledMma_PV;
using Layout = typename Traits::LayoutConvertor;

using SmemLayoutQ = typename Traits::SmemLayoutQ;
using SmemLayoutKV = typename Traits::SmemLayoutKV;
using SmemLayoutP = typename Traits::SmemLayoutP;
using SmemLayoutQRope = typename Traits::SmemLayoutQRope;
using SmemLayoutKRope = typename Traits::SmemLayoutKRope;
using SmemLayoutVt = typename Traits::SmemLayoutVt;
Expand All @@ -61,6 +63,8 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(

using SmemTiledCopyQ = typename Traits::SmemTiledCopyQ;
using SmemTiledCopyK = typename Traits::SmemTiledCopyK;
using SmemTiledCopyS = typename Traits::SmemTiledCopyS;
using SmemTiledCopyP = typename Traits::SmemTiledCopyP;
using SmemTiledCopyVt = typename Traits::SmemTiledCopyVt;
using SmemTiledCopyO = typename Traits::SmemTiledCopyO;

Expand Down Expand Up @@ -102,14 +106,18 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(
extern __shared__ char smem[];
DType* q_smem = (DType*)smem;
DType* kv_smem = q_smem + cosize(SmemLayoutQ{});
DType* q_rope_smem = kv_smem + cosize(SmemLayoutKV{});
DType* p_smem = kv_smem + cosize(SmemLayoutKV{});
DType* q_rope_smem = p_smem + cosize(SmemLayoutP{});
DType* k_rope_smem = q_rope_smem + cosize(SmemLayoutQRope{});

// (BLK_M, BLK_K, STAGES), k-major
Tensor sQ = make_tensor(make_smem_ptr(q_smem), SmemLayoutQ{});
// (BLK_N, BLK_K, STAGES), k-major
Tensor sK = make_tensor(make_smem_ptr(kv_smem), SmemLayoutKV{});

// (BLK_M, BLK_N), k-major
Tensor sP = make_tensor(make_smem_ptr(p_smem), SmemLayoutP{});

// (BLK_M, ROPE_HEAD_DIM), k-major
Tensor sQ_rope = make_tensor(make_smem_ptr(q_rope_smem), SmemLayoutQRope{});
// (BLK_N, ROPE_HEAD_DIM), k-major
Expand Down Expand Up @@ -157,19 +165,19 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(
cute::copy(gmem_tiled_copy_KV, tKgK_rope, tKsK_rope);
};

TiledMma tiled_mma;
auto thr_mma = tiled_mma.get_slice(tidx);
TiledMma_QK tiled_mma_qk;
auto thr_mma_qk = tiled_mma_qk.get_slice(tidx);
// GEMM-I: S = Q@K.T
// sQ/sK: (BLK_M, BLK_K, STAGES)
auto tSrQ = partition_fragment_A(
thr_mma, sQ(_, _, _0{}), _, _2{}); // (MMA, MMA_M, _2)
thr_mma_qk, sQ(_, _, _0{}), _, _2{}); // (MMA, MMA_M, _2)
auto tSrK = partition_fragment_B(
thr_mma, sK(_, _, _0{}), _, _2{}); // (MMA, MMA_N, _2)
thr_mma_qk, sK(_, _, _0{}), _, _2{}); // (MMA, MMA_N, _2)

auto tSrQ_rope =
partition_fragment_A(thr_mma, sQ_rope, _, _2{}); // (MMA, MMA_M, _2)
partition_fragment_A(thr_mma_qk, sQ_rope, _, _2{}); // (MMA, MMA_M, _2)
auto tSrK_rope =
partition_fragment_B(thr_mma, sK_rope, _, _2{}); // (MMA, MMA_N, _2)
partition_fragment_B(thr_mma_qk, sK_rope, _, _2{}); // (MMA, MMA_N, _2)

// s2r tiled copy for qkv
SmemTiledCopyQ smem_tiled_copy_Q;
Expand Down Expand Up @@ -216,7 +224,7 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(
cute::copy(
smem_tiled_copy_K, tCsK_s(_, _, next_k), tCrK(_, _, (next_k & 1)));
}
cute::gemm(tiled_mma, tSrQ(_, _, (k & 1)), tSrK(_, _, (k & 1)), tSrS);
cute::gemm(tiled_mma_qk, tSrQ(_, _, (k & 1)), tSrK(_, _, (k & 1)), tSrS);
}
};

Expand All @@ -236,14 +244,29 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(
tCsK_rope(_, _, next_k),
tCrK_rope(_, _, (next_k & 1)));
}
cute::gemm(
tiled_mma, tSrQ_rope(_, _, (k & 1)), tSrK_rope(_, _, (k & 1)), tSrS);
cute::gemm(tiled_mma_qk,
tSrQ_rope(_, _, (k & 1)),
tSrK_rope(_, _, (k & 1)),
tSrS);
}
};

// GEMM-II: O = softmax(S)@V
TiledMma_PV tiled_mma_pv;
auto thr_mma_pv = tiled_mma_pv.get_slice(tidx);
// sS: (BLK_M, BLK_N)
// (MMA, MMA_M, _2)
auto tOrP = partition_fragment_A(thr_mma_pv, sP, _, _2{});
// sVt: (BLK_K, BLK_N, STAGES)
// (MMA, MMA_N, _2)
auto tOrVt = partition_fragment_B(thr_mma, sVt(_, _, _0{}), _, _2{});
auto tOrVt = partition_fragment_B(thr_mma_pv, sVt(_, _, _0{}), _, _2{});

SmemTiledCopyP smem_tiled_copy_P;
auto smem_thr_copy_P = smem_tiled_copy_P.get_slice(tidx);
// (CPY, CPY_M, CPY_K)
auto tCsP = smem_thr_copy_P.partition_S(sP);
// (CPY, CPY_M, _2)
auto tCrP = smem_thr_copy_P.retile_D(tOrP);

SmemTiledCopyVt smem_tiled_copy_Vt;
auto smem_thr_copy_Vt = smem_tiled_copy_Vt.get_slice(tidx);
Expand All @@ -254,27 +277,44 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(

// O = softmax(S)*V
// tOrS: (MMA,MMA_M,MMA_K)
auto compute_pv = [&](const auto& tOrS, auto& tOrO, int s) {
auto compute_sv = [&](auto& tOrO, int s) {
// (MMA,MMA_M,MMA_N, STAGES)
auto tOrO_s = tOrO(_, _, _, s);

// (CPY, CPY_N, CPY_K, STAGES)
auto tCsVt_s = tCsVt(_, _, _, s);
// tCsVt_s: (CPY, CPY_N, CPY_K) => tCrVt: (CPY, CPY_N, _2)
cute::copy(smem_tiled_copy_P, tCsP(_, _, _0{}), tCrP(_, _, _0{}));
cute::copy(smem_tiled_copy_Vt, tCsVt_s(_, _, _0{}), tCrVt(_, _, _0{}));

CUTE_UNROLL
for (int k = 0; k < size<2>(tOrS); ++k) {
if (k != size<2>(tOrS) - 1) {
for (int k = 0; k < size<2>(tCsVt_s); ++k) {
if (k != size<2>(tCsVt_s) - 1) {
const auto next_k = k + 1;
cute::copy(
smem_tiled_copy_P, tCsP(_, _, next_k), tCrP(_, _, (next_k & 1)));
cute::copy(smem_tiled_copy_Vt,
tCsVt_s(_, _, next_k),
tCrVt(_, _, (next_k & 1)));
}
cute::gemm(tiled_mma, tOrS(_, _, k), tOrVt(_, _, (k & 1)), tOrO_s);
cute::gemm(
tiled_mma_pv, tCrP(_, _, (k & 1)), tOrVt(_, _, (k & 1)), tOrO_s);
}
};

SmemTiledCopyS smem_tiled_copy_S;
auto smem_thr_copy_S = smem_tiled_copy_S.get_slice(tidx);

auto save_scores = [&](const auto& tSrS) {
// cast Accumulator to Element type
auto tSrS_ = make_tensor_like<DType>(tSrS);
fast_cast(tSrS, tSrS_);
// copy scores from rmem to smem
auto tCrS = smem_thr_copy_S.retile_S(tSrS_);
auto tCsS = smem_thr_copy_S.partition_D(sP);
cute::copy(smem_tiled_copy_S, tCrS, tCsS);
};

// tOrO: (MMA,MMA_M,MMA_K,STAGES)
auto epilogue = [&](const auto& tOrO) {
// write output to gmem
Expand Down Expand Up @@ -309,7 +349,7 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(
};

// output accumulator: (MMA, MMA_M, MMA_K, STAGES)
auto tOrO = partition_fragment_C(thr_mma, Shape<_BLK_M, _BLK_K, _STAGES>{});
auto tOrO = partition_fragment_C(tiled_mma_pv, Shape<_BLK_M, _BLK_K, _STAGES>{});
auto tOrO_mn = make_tensor(tOrO.data(), Layout::to_mns(tOrO.layout()));
clear(tOrO);

Expand All @@ -323,7 +363,6 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(
for (int s = 0; s < kStages; ++s) {
produce_q(s);
}

// produce k_rope: [q_rope, q...] => [q_rope, q..., k_rope, kv...]
produce_k_rope(0);
cp_async_fence();
Expand All @@ -341,7 +380,7 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(
CUTE_NO_UNROLL
for (int ni = n_block_min; ni < n_block_max; ++ni) {
// attention score accumulator, (MMA,MMA_M,MMA_N)
auto tSrS = partition_fragment_C(tiled_mma, Shape<_BLK_M, _BLK_N>{});
auto tSrS = partition_fragment_C(tiled_mma_qk, Shape<_BLK_M, _BLK_N>{});
auto tSrS_mn = make_tensor(tSrS.data(), Layout::to_mn(tSrS.layout()));
clear(tSrS);

Expand All @@ -363,36 +402,35 @@ __global__ __launch_bounds__(Traits::kThreadNum) void mla_kernel_sm80(
cp_async_fence();
}

softmax.rescale(tSrS_mn, tOrO_mn);
// softmax.rescale(tSrS_mn, tOrO_mn);

// save tSrS from rmem to smem
save_scores(tSrS);
__syncthreads();

// 3> O = softmax(S)*V
// cast scores from Accumulator to Element
auto tSrS_ = make_tensor_like<DType>(tSrS);
fast_cast(tSrS, tSrS_);
// convert layout from gemm-I C to gemm-II A
auto tOrS = make_tensor(tSrS_.data(), Layout::to_mma_a(tSrS_.layout()));
const auto next_ni = ni + 1;
if (next_ni != n_block_max) {
produce_k_rope(next_ni);
cp_async_fence();
CUTE_UNROLL
for (int s = 0; s < kStages; ++s) {
compute_pv(tOrS, tOrO, s);
compute_sv(tOrO, s);
produce_kv(next_ni, s);
cp_async_fence();
}
} else {
CUTE_UNROLL
for (int s = 0; s < kStages; ++s) {
compute_pv(tOrS, tOrO, s);
compute_sv(tOrO, s);
}
}
}

// ############### Epilogue ###############

// normalize output: o /= rowsum
softmax.finalize(tOrO_mn);
// softmax.finalize(tOrO_mn);

// write output to gmem
epilogue(tOrO);
Expand Down
7 changes: 5 additions & 2 deletions src/kernels/attention/mla_kernel_sm80_test.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,7 @@ torch::Tensor mla_sm80(
/*ROPE_HEAD_DIM=*/64,
/*BLK_M=*/64,
/*BLK_N=*/64,
/*BLK_K=*/128>;
/*BLK_K=*/64>;
launch_mla_kernel_sm80<Traits>(params, nullptr);
return out;
}
Expand Down Expand Up @@ -109,6 +109,9 @@ TEST_P(MLAKernelTest, MLA) {

auto ref_out = mla_batch_ref(q, kv, q_rope, k_rope, sm_scale);
auto out = mla_sm80(q, kv, q_rope, k_rope, sm_scale);
std::cerr << "max diff: " << (ref_out - out).abs().max() << std::endl;
// std::cerr << "ref_out: " << ref_out << std::endl;
// std::cerr << "out: " << out << std::endl;
EXPECT_TRUE(torch::allclose(out, ref_out, /*rtol=*/1e-3, /*atol=*/1e-3));
}

Expand All @@ -119,7 +122,7 @@ INSTANTIATE_TEST_SUITE_P(
::testing::Values(1), // batch_size
::testing::Values(64), // q_len
::testing::Values(64), // kv_len
::testing::Values(8), // n_heads
::testing::Values(1), // n_heads
::testing::Values(256), // head_dim
::testing::Values(64) // rope_head_dim
));
Expand Down
4 changes: 2 additions & 2 deletions src/kernels/attention/mla_ref.h
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Original file line number Diff line number Diff line change
Expand Up @@ -29,10 +29,10 @@ inline torch::Tensor mla_batch_ref(
auto scores = torch::einsum("bqhr,bkr->bqhk", {q_, kv_}) +
torch::einsum("bqhp,bkp->bqhk", {q_rope_, k_rope_});
// apply scale
scores *= sm_scale;
// scores *= sm_scale;

// safe softmax
scores = torch::softmax(scores, /*dim=*/-1);
// scores = torch::softmax(scores, /*dim=*/-1);

// score * value => [batch_size, q_len, n_heads, kv_lora_rank]
return torch::einsum("bqhk,bkr->bqhr", {scores, kv_}).type_as(q);
Expand Down
14 changes: 12 additions & 2 deletions src/kernels/attention/mla_sm80_bench.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -14,18 +14,28 @@ using namespace llm;
[&] { \
if (HEAD_DIM_V <= 64) { \
constexpr static int HEAD_DIM_NAME = 64; \
constexpr static int BLK_N = 64; \
constexpr static int BLK_K = 64; \
return __VA_ARGS__(); \
} else if (HEAD_DIM_V <= 128) { \
constexpr static int HEAD_DIM_NAME = 128; \
constexpr static int BLK_N = 64; \
constexpr static int BLK_K = 64; \
return __VA_ARGS__(); \
} else if (HEAD_DIM_V <= 256) { \
constexpr static int HEAD_DIM_NAME = 256; \
constexpr static int BLK_N = 64; \
constexpr static int BLK_K = 64; \
return __VA_ARGS__(); \
} else if (HEAD_DIM_V <= 384) { \
constexpr static int HEAD_DIM_NAME = 384; \
constexpr static int BLK_N = 64; \
constexpr static int BLK_K = 64; \
return __VA_ARGS__(); \
} else if (HEAD_DIM_V <= 512) { \
constexpr static int HEAD_DIM_NAME = 512; \
constexpr static int BLK_N = 32; \
constexpr static int BLK_K = 64; \
return __VA_ARGS__(); \
} else { \
assert(false); \
Expand Down Expand Up @@ -87,8 +97,8 @@ void mla_bench_sm80(nvbench::state& state) {
HEAD_DIM,
/*ROPE_HEAD_DIM=*/64,
/*BLK_M=*/64,
/*BLK_N=*/64,
/*BLK_K=*/64>;
BLK_N,
BLK_K>;

launch_mla_kernel_sm80<Traits>(params, launch.get_stream());
});
Expand Down
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