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只需百行代码,让H100提速30%,斯坦福开源全新AI加速框架
只需百行代码,让H100提速30%,斯坦福开源全新AI加速框架
5月前
提高 GPU 利用率,就是这么简单。
简单,ThunderKittens 写起来非常简单。 可扩展性,如果用户需要 ThunderKittens 无法提供的功能,可以进行功能扩展。 速度快。
80 GB HBM3,带宽为 3 TB/s(实际上带宽会少一些); 50 MB 二级缓存,带宽 12 TB/s,在 GPU 上分成两个 25MB 的部分,通过 crossbar 连接; 132 个流多处理器 (SM,streaming multiprocessors)。
WGMMA 指令是必需的,但使用起来也非常令人恼火; 共享内存实际上并没有那么快,并且需要非常小心; 地址生成成本很高; 占用率仍然有帮助,寄存器通常是关键资源。
寄存器 tile—— 寄存器文件中的 2D 张量。 寄存器向量 —— 寄存器文件中的 1D 张量。 共享 tile—— 共享内存中的 2D 张量。 共享向量 —— 共享内存中的 1D 张量。
一元运算,如 exp 二元运算,如 mul 行 / 列操作,如 row_sum
using namespace kittens; // this kernel only handles headdim=64 for simplicity. Also n should be a multiple of 256 here.
__global__ void attend_ker64(int n, const bf16* __restrict__ __q__, const bf16* __restrict__ __k__, const bf16* __restrict__ __v__, bf16* __o__) {
auto warpid = kittens::warpid();
auto block_start = blockIdx.x*(n*64);
const bf16 *_q = __q__ + block_start, *_k = __k__ + block_start, *_v = __v__ + block_start;
bf16 *_o = __o__ + block_start;
extern __shared__ alignment_dummy __shm[]; // this is the CUDA shared memory
shared_allocator al((int*)&__shm[0]);
// K and V live in shared memory -- this is about all that will fit.
st_bf_1x4<ducks::st_layout::swizzle> (&k_smem)[NUM_WORKERS] = al.allocate<st_bf_1x4<ducks::st_layout::swizzle>, NUM_WORKERS>();
st_bf_1x4<ducks::st_layout::swizzle> (&v_smem)[NUM_WORKERS] = al.allocate<st_bf_1x4<ducks::st_layout::swizzle>, NUM_WORKERS>();
// Initialize all of the register tiles.
rt_bf_1x4<> q_reg, k_reg, v_reg; // v_reg need to be swapped into col_l
rt_fl_1x1<> att_block;
rt_bf_1x1<> att_block_mma;
rt_fl_1x4<> o_reg;
rt_fl_1x1<>::col_vec max_vec_last, max_vec; // these are column vectors for the attention block
rt_fl_1x1<>::col_vec norm_vec_last, norm_vec; // these are column vectors for the attention block
int qo_blocks = n / (q_reg.rows*NUM_WORKERS), kv_blocks = n / (q_reg.rows*NUM_WORKERS);
for(auto q_blk = 0; q_blk < qo_blocks; q_blk++) {
// each warp loads its own Q tile of 16x64, and then multiplies by 1/sqrt(d)
load(q_reg, _q + (q_blk*NUM_WORKERS + warpid)*q_reg.num_elements, q_reg.cols);
mul(q_reg, q_reg, __float2bfloat16(0.125f)); // temperature adjustment
// zero flash attention L, M, and O registers.
neg_infty(max_vec); // zero registers for the Q chunk
zero(norm_vec);
zero(o_reg);
// iterate over k, v for these q's that have been loaded
for(auto kv_idx = 0; kv_idx < kv_blocks; kv_idx++) {
// each warp loads its own chunk of k, v into shared memory
load(v_smem[warpid], _v + (kv_idx*NUM_WORKERS + warpid)*q_reg.num_elements, q_reg.cols);
load(k_smem[warpid], _k + (kv_idx*NUM_WORKERS + warpid)*q_reg.num_elements, q_reg.cols);
__syncthreads(); // we need to make sure all memory is loaded before we can begin the compute phase
// now each warp goes through all of the subtiles, loads them, and then does the flash attention internal alg.
for(int subtile = 0; subtile < NUM_WORKERS; subtile++) {
load(k_reg, k_smem[subtile]); // load k from shared into registers
zero(att_block); // zero 16x16 attention tile
mma_ABt(att_block, q_reg, k_reg, att_block); // [email protected]
copy(norm_vec_last, norm_vec);
copy(max_vec_last, max_vec);
row_max(max_vec, att_block, max_vec); // accumulate onto the max_vec
sub_row(att_block, att_block, max_vec); // subtract max from attention -- now all <=0
exp(att_block, att_block); // exponentiate the block in-place.
sub(max_vec_last, max_vec_last, max_vec); // subtract new max from old max to find the new normalization.
exp(max_vec_last, max_vec_last); // exponentiate this vector -- this is what we need to normalize by.
mul(norm_vec, norm_vec, max_vec_last); // and the norm vec is now normalized.
row_sum(norm_vec, att_block, norm_vec); // accumulate the new attention block onto the now-rescaled norm_vec
div_row(att_block, att_block, norm_vec); // now the attention block is correctly normalized
mul(norm_vec_last, norm_vec_last, max_vec_last); // normalize the previous norm vec according to the new max
div(norm_vec_last, norm_vec_last, norm_vec); // normalize the previous norm vec according to the new norm
copy(att_block_mma, att_block); // convert to bf16 for mma_AB
load(v_reg, v_smem[subtile]); // load v from shared into registers.
rt_bf_1x4<ducks::rt_layout::col> &v_reg_col = swap_layout_inplace(v_reg); // this is a reference and the call has invalidated v_reg
mul_row(o_reg, o_reg, norm_vec_last); // normalize o_reg in advance of mma_AB'ing onto it
mma_AB(o_reg, att_block_mma, v_reg_col, o_reg); // mfma onto o_reg with the local attention@V matmul.
}
__syncthreads(); // we need to make sure all warps are done before we can start loading the next kv chunk
}
store(_o + (q_blk*NUM_WORKERS + warpid)*q_reg.num_elements, o_reg, q_reg.cols); // write out o. compiler has an issue with register usage if d is made constexpr q_reg.rows :/
}
}
template<int D>
__global__ __launch_bounds__((NUM_WORKERS)*kittens::WARP_THREADS, 2)
void fwd_attend_ker_dim(int N, const CUtensorMap* tma_q, const CUtensorMap* tma_k, const CUtensorMap* tma_v, CUtensorMap* tma_o) {
extern __shared__ int __shm[]; // this is the CUDA shared memory
tma_swizzle_allocator al((int*)&__shm[0]);
constexpr int tile_width = fwd_attend_ker_tile_dims<D>::tile_width; // constants
constexpr int qo_height = fwd_attend_ker_tile_dims<D>::qo_height;
constexpr int kv_height = fwd_attend_ker_tile_dims<D>::kv_height;
st_bf<qo_height, tile_width, layout_q> (&q_smem) [NUM_WARPGROUPS] = al.allocate<st_bf<qo_height, tile_width, layout_q>, NUM_WARPGROUPS>();
st_bf<kv_height, tile_width, layout_k> (&k_smem)[2][NUM_WORKERS_KV] = al.allocate<st_bf<kv_height, tile_width, layout_k>, 2, NUM_WORKERS_KV>();
st_bf<kv_height, tile_width, layout_v> (&v_smem)[2][NUM_WORKERS_KV] = al.allocate<st_bf<kv_height, tile_width, layout_v>, 2, NUM_WORKERS_KV>();
int tic = 0, toc = 1;
rt_fl<1, kv_height> att_block;
rt_bf<1, kv_height> att_block_mma;
rt_fl<1, qo_height> o_prev;
col_vec<rt_fl<1, kv_height>> max_vec_last, max_vec;
col_vec<rt_fl<1, kv_height>> norm_vec_last, norm_vec;
int warpid = kittens::warpid();
int warpgroupid = warpid/kittens::WARPGROUP_WARPS;
int kv_blocks = N / (NUM_WORKERS_KV*k_smem[0][0].rows);
__shared__ uint64_t qsmem_barrier, kvsmem_barrier;//, vsmem_barrier;
int q_phasebit = 0;
int kv_phasebit = 0;
if (threadIdx.x == 0) {
tma::init_barrier<st_bf<qo_height, tile_width, layout_q>, NUM_WARPGROUPS>(qsmem_barrier, 1);
tma::init_barrier<st_bf<kv_height, tile_width, layout_k>, NUM_WORKERS_KV*2>(kvsmem_barrier, 1);
}
if (warpid == 0) {
for (int wg = 0; wg < NUM_WORKERS/kittens::WARPGROUP_WARPS; wg++) { // load q
int tile_idx = (blockIdx.y * NUM_WARPGROUPS * gridDim.x) + (blockIdx.x * NUM_WARPGROUPS) + wg;
tma::load_async((q_smem[wg]), tma_q, qsmem_barrier, tile_idx);
}
for (int w = 0; w < NUM_WORKERS_KV; w++) { // load k, v
int tile_idx = (blockIdx.y * NUM_WORKERS_KV * kv_blocks) + (0 * NUM_WORKERS_KV) + w;
tma::load_async((k_smem[tic][w]), tma_k, kvsmem_barrier, tile_idx);
tma::load_async((v_smem[tic][w]), tma_v, kvsmem_barrier, tile_idx);
}
}
neg_infty(max_vec); // zero registers for the Q chunk
zero(norm_vec);
zero(o_prev);
__syncthreads();
tma::arrive_and_wait(qsmem_barrier, q_phasebit);
q_phasebit ^= 1;
if constexpr (D == 64) { warpgroup::mul(q_smem[warpgroupid], q_smem[warpgroupid], __float2bfloat16(0.125f)); }
else { warpgroup::mul(q_smem[warpgroupid], q_smem[warpgroupid], __float2bfloat16(0.08838834764f)); }
for (auto kv_idx = 0; kv_idx < kv_blocks; kv_idx++, tic ^= 1, toc ^= 1) {
tma::arrive_and_wait(kvsmem_barrier, kv_phasebit);
kv_phasebit ^= 1;
__syncthreads();
if (warpid == 0) {
tma::set_bytes(kvsmem_barrier, 2 * NUM_WORKERS_KV * k_smem[0][0].num_elements * sizeof(bf16));
if (kv_idx + 1 < kv_blocks) {
for (int w = 0; w < NUM_WORKERS_KV; w++) {
int tile_idx = (blockIdx.y * NUM_WORKERS_KV * kv_blocks) + ((kv_idx + 1) * NUM_WORKERS_KV) + w;
tma::load_async((k_smem[toc][w]), tma_k, kvsmem_barrier, tile_idx);
tma::load_async((v_smem[toc][w]), tma_v, kvsmem_barrier, tile_idx);
}
}
}
warpgroup::mma_fence(att_block);
warpgroup::mm_ABt(att_block, q_smem[warpgroupid], k_smem[tic][0]);
warpgroup::mma_commit_group();
copy(norm_vec_last, norm_vec);
copy(max_vec_last, max_vec);
warpgroup::mma_async_wait();
row_max(max_vec, att_block, max_vec); // accumulate onto the max_vec
sub_row(att_block, att_block, max_vec);
exp(att_block, att_block);
sub(max_vec_last, max_vec_last, max_vec);
exp(max_vec_last, max_vec_last);
mul(norm_vec, norm_vec, max_vec_last);
row_sum(norm_vec, att_block, norm_vec); // accumulate onto the norm_vec
div_row(att_block, att_block, norm_vec);
mul(norm_vec_last, norm_vec_last, max_vec_last);
div(norm_vec_last, norm_vec_last, norm_vec);
copy(att_block_mma, att_block); // convert to bf16 for mma
mul_row(o_prev, o_prev, norm_vec_last); // normalize o_prev in advance of mma'ing onto it
warpgroup::mma_fence(o_prev);
warpgroup::mma_AB(o_prev, att_block_mma, v_smem[tic][0]);
warpgroup::mma_commit_group();
}
auto (*o_smem) = reinterpret_cast<st_bf<qo_height, tile_width, layout_o>(*)>(q_smem); // reuse q memory
warpgroup::store(o_smem[warpgroupid], o_prev);
__syncthreads();
if (warpid % 4 == 0) { // store o
int tile_idx = (blockIdx.y * NUM_WARPGROUPS * gridDim.x) + (blockIdx.x * NUM_WARPGROUPS) + warpgroupid;
tma::store_async(tma_o, (o_smem[warpgroupid]), tile_idx);
tma::store_commit_group();
}
tma::store_async_wait();
}
H100 SXM 上各种配置的 FlashAttention-2(Pytorch)与 ThunderKittens 的比较。
使用 ThunderKittens 可以非常快地实现线性注意力。
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