# ruff: noqa import math import torch import tilelang from tilelang import language as T from tilelang.profiler import do_bench def is_hip(): return False def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False): bsz, num_head, downsample_len, _ = x.shape # N_CTX = downsample_len * BLOCK sparse_index = torch.topk(x, topk, dim=-1).indices dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device) dense_mask.scatter_(-1, sparse_index, True) if use_dense_for_last_block: dense_mask[:, :, -2:, :] = True dense_mask.tril_() return dense_mask def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=False): dense_mask = x > threshold if use_dense_for_last_block: dense_mask[:, :, -2:, :] = True dense_mask.tril_() return dense_mask def blocksparse_flashattn(batch, heads, seq_len, dim, downsample_len, is_causal): block_M = 64 block_N = 64 num_stages = 2 threads = 128 scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) shape = [batch, heads, seq_len, dim] block_mask_shape = [batch, heads, downsample_len, downsample_len] dtype = "float16" accum_dtype = "float" block_mask_dtype = "bool" def kernel_func(block_M, block_N, num_stages, threads): @T.macro def MMA0( K: T.Tensor(shape, dtype), Q_shared: T.SharedBuffer([block_M, dim], dtype), K_shared: T.SharedBuffer([block_N, dim], dtype), acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), k: T.int32, bx: T.int32, by: T.int32, bz: T.int32, ): T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared) if is_causal: for i, j in T.Parallel(block_M, block_N): acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) else: T.clear(acc_s) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) @T.macro def MMA1( V: T.Tensor(shape, dtype), V_shared: T.SharedBuffer([block_M, dim], dtype), acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), k: T.int32, by: T.int32, bz: T.int32, ): T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) @T.macro def Softmax( acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), scores_max: T.FragmentBuffer([block_M], accum_dtype), scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), scores_scale: T.FragmentBuffer([block_M], accum_dtype), scores_sum: T.FragmentBuffer([block_M], accum_dtype), logsum: T.FragmentBuffer([block_M], accum_dtype), ): T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) # To do causal softmax, we need to set the scores_max to 0 if it is -inf # This process is called Check_inf in FlashAttention3 code, and it only need to be done # in the first ceil_div(kBlockM, kBlockN) steps. # for i in T.Parallel(block_M): # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) for i in T.Parallel(block_M): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, block_N): # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - # max * log_2(e)) This allows the compiler to use the ffma # instruction instead of fadd and fmul separately. acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) for i in T.Parallel(block_M): logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] T.copy(acc_s, acc_s_cast) @T.macro def Rescale( acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), scores_scale: T.FragmentBuffer([block_M], accum_dtype), ): for i, j in T.Parallel(block_M, dim): acc_o[i, j] *= scores_scale[i] @T.prim_func def main( Q: T.Tensor(shape, dtype), K: T.Tensor(shape, dtype), V: T.Tensor(shape, dtype), BlockSparseMask: T.Tensor(block_mask_shape, block_mask_dtype), Output: T.Tensor(shape, dtype), ): with T.Kernel( T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim], dtype) O_shared = T.alloc_shared([block_M, dim], dtype) acc_s = T.alloc_fragment([block_M, block_N], accum_dtype) acc_s_cast = T.alloc_fragment([block_M, block_N], dtype) acc_o = T.alloc_fragment([block_M, dim], accum_dtype) scores_max = T.alloc_fragment([block_M], accum_dtype) scores_max_prev = T.alloc_fragment([block_M], accum_dtype) scores_scale = T.alloc_fragment([block_M], accum_dtype) scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) block_mask = T.alloc_local([downsample_len], block_mask_dtype) T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) for vj in T.serial(downsample_len): block_mask[vj] = BlockSparseMask[bz, by, bx, vj] loop_range = ( T.min(T.ceildiv(seq_len, block_N), T.ceildiv( (bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N)) for k in T.Pipelined(loop_range, num_stages=num_stages): if block_mask[k]: MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, logsum) Rescale(acc_o, scores_scale) MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) return main return kernel_func(block_M, block_N, num_stages, threads) def benchmark_topk_sparse_attention(): from benchmark_configs import configs torch.manual_seed(0) # Config for BATCH, N_HEADS, SEQ_LEN, D_HEAD, TOPK, BLOCK in configs: # Create inputs q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) # Create sparse mask (downsampled to block level) downsample_factor = BLOCK downsample_len = math.ceil(SEQ_LEN / downsample_factor) x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device='cuda', dtype=torch.bfloat16) x_ds[:, :, :, 0] = 100 block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) program = blocksparse_flashattn( BATCH, N_HEADS, SEQ_LEN, D_HEAD, downsample_len, is_causal=True) kernel = tilelang.compile(program, out_idx=4) def benchmark_fn(): # Compute reference # Expand block mask to full attention matrix kernel(q, k, v, block_mask) ref_latency = do_bench( benchmark_fn, warmup=10, rep=100, ) print( f"BATCH: {BATCH}, N_HEADS: {N_HEADS}, SEQ_LEN: {SEQ_LEN}, D_HEAD: {D_HEAD}, TOPK: {TOPK}, BLOCK: {BLOCK}, ref_latency: {ref_latency}" ) if __name__ == "__main__": benchmark_topk_sparse_attention()