import torch import torch.nn.functional as F import tilelang from tilelang.autotuner import * import tilelang.language as T from einops import rearrange, einsum import argparse @tilelang.jit(out_idx=[6]) def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split): scale = (1.0 / (dim + pe_dim))**0.5 * 1.44269504 # log2(e) dtype = "float16" accum_dtype = "float" kv_group_num = heads // kv_head_num VALID_BLOCK_H = min(block_H, kv_group_num) assert kv_head_num == 1, "kv_head_num must be 1" h_dim = dim // 2 @T.macro def flash_attn( Q: T.Tensor([batch, heads, dim], dtype), Q_pe: T.Tensor([batch, heads, pe_dim], dtype), KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), Output: T.Tensor([batch, heads, dim], dtype), ): with T.Kernel(heads // min(block_H, kv_group_num), batch, threads=256) as (hid, bid): # smem_sQ Q_shared_l = T.alloc_shared([block_H, h_dim], dtype) Q_shared_r = T.alloc_shared([block_H, h_dim], dtype) Q_pe_shared = T.alloc_shared([block_H, pe_dim], dtype) Q_pe_local_0 = T.alloc_fragment([block_H, pe_dim], dtype) Q_pe_local_1 = T.alloc_fragment([block_H, pe_dim], dtype) # smem_sK0 KV_shared_0_l = T.alloc_shared([block_N, h_dim], dtype) KV_shared_0_r = T.alloc_shared([block_N, h_dim], dtype) K_pe_shared_0 = T.alloc_shared([block_N, pe_dim], dtype) # smem_sK1 KV_shared_1_l = T.alloc_shared([block_N, h_dim], dtype) KV_shared_1_r = T.alloc_shared([block_N, h_dim], dtype) K_pe_shared_1 = T.alloc_shared([block_N, pe_dim], dtype) # smem_sP0 SP0_shared = T.alloc_shared([block_H, block_N], dtype) # smem_sP1 reuse Q_pe_shared SP1_shared = Q_pe_shared # smem_sM scores_max = T.alloc_shared([block_H], accum_dtype) # smem_sScale0 scores_scale_0 = T.alloc_shared([block_H], accum_dtype) # smem_sScale1 scores_scale_1 = T.alloc_shared([block_H], accum_dtype) logsum = T.alloc_shared([block_H], accum_dtype) O_shared_l = Q_shared_l O_shared_r = Q_shared_r acc_s_0 = T.alloc_fragment([block_H, block_N], accum_dtype) acc_s_0_cast = T.alloc_fragment([block_H, block_N], dtype) acc_s_1 = T.alloc_fragment([block_H, block_N], accum_dtype) acc_s_1_cast = T.alloc_fragment([block_H, block_N], dtype) acc_o_l = T.alloc_fragment([block_H, h_dim], accum_dtype) acc_o_r = T.alloc_fragment([block_H, h_dim], accum_dtype) scores_max_0 = T.alloc_fragment([block_H], accum_dtype) scores_max_1 = T.alloc_fragment([block_H], accum_dtype) scores_max_prev_0 = T.alloc_fragment([block_H], accum_dtype) scores_max_prev_1 = T.alloc_fragment([block_H], accum_dtype) scores_sum_0 = T.alloc_fragment([block_H], accum_dtype) scores_sum_1 = T.alloc_fragment([block_H], accum_dtype) logsum_0 = T.alloc_fragment([block_H], accum_dtype) logsum_1 = T.alloc_fragment([block_H], accum_dtype) cur_kv_head = hid // (kv_group_num // block_H) T.annotate_layout({ O_shared_l: tilelang.layout.make_swizzled_layout(O_shared_l), O_shared_r: tilelang.layout.make_swizzled_layout(O_shared_r), }) # barriers_Q q_shared_ready_barrier = T.alloc_barrier(arrive_count=256) # barriers_K0 kv_shared_0_l_is_ready = T.alloc_barrier(arrive_count=128) kv_shared_0_r_is_ready = T.alloc_barrier(arrive_count=128) kv_shared_0_pe_is_ready = T.alloc_barrier(arrive_count=128) # barriers_K1 kv_shared_1_l_is_ready = T.alloc_barrier(arrive_count=128) kv_shared_1_r_is_ready = T.alloc_barrier(arrive_count=128) kv_shared_1_pe_is_ready = T.alloc_barrier(arrive_count=128) # redundant barriers score_max_0_ready_barrier = T.alloc_barrier(arrive_count=128) scale_1_ready_barrier = T.alloc_barrier(arrive_count=128) p0_1_1_ready_barrier = T.alloc_barrier(arrive_count=128) lse_0_ready_barrier = T.alloc_barrier(arrive_count=128) lse_1_ready_barrier = T.alloc_barrier(arrive_count=128) s_shared_ready_barrier = T.alloc_barrier(arrive_count=128) tx = T.get_thread_binding() T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :h_dim], Q_shared_l) T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, h_dim:], Q_shared_r) T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_pe_shared) T.barrier_arrive(q_shared_ready_barrier) T.barrier_wait(q_shared_ready_barrier, 0) T.fill(scores_max, -T.infinity(accum_dtype)) loop_range = T.ceildiv(seqlen_kv, (block_N * 2)) if tx < 128: T.copy(Q_pe_shared, Q_pe_local_0) T.fill(acc_o_l, 0) T.fill(logsum_0, 0) T.copy(KV[bid, block_N:2 * block_N, cur_kv_head, :h_dim], KV_shared_1_l) T.barrier_arrive(kv_shared_1_l_is_ready) T.copy(KV[bid, block_N:2 * block_N, cur_kv_head, h_dim:], KV_shared_1_r) T.barrier_arrive(kv_shared_1_r_is_ready) T.copy(K_pe[bid, block_N:2 * block_N, cur_kv_head, :], K_pe_shared_1) T.barrier_arrive(kv_shared_1_pe_is_ready) for k in T.serial(loop_range): T.barrier_wait(kv_shared_0_l_is_ready, k % 2) T.gemm( Q_shared_l, KV_shared_0_l, acc_s_0, transpose_B=True, clear_accum=True, wg_wait=-1) T.barrier_wait(kv_shared_0_r_is_ready, k % 2) T.gemm(Q_shared_r, KV_shared_0_r, acc_s_0, transpose_B=True, wg_wait=-1) T.barrier_wait(kv_shared_0_pe_is_ready, k % 2) T.gemm(Q_pe_local_0, K_pe_shared_0, acc_s_0, transpose_B=True, wg_wait=-1) T.wait_wgmma(0) # Step 3. T.copy(scores_max, scores_max_0) T.copy(scores_max_0, scores_max_prev_0) T.fill(scores_max_0, -T.infinity(accum_dtype)) T.reduce_max(acc_s_0, scores_max_0, dim=1, clear=False) T.copy(scores_max_0, scores_max) # Step 4. for i, j in T.Parallel(block_H, block_N): acc_s_0[i, j] = T.exp2(acc_s_0[i, j] * scale - scores_max[i] * scale) for i in T.Parallel(block_H): scores_scale_0[i] = T.exp2(scores_max_prev_0[i] * scale - scores_max[i] * scale) T.reduce_sum(acc_s_0, scores_sum_0, dim=1) # Step 5. T.copy(acc_s_0, acc_s_0_cast) for i, j in T.Parallel(block_H, h_dim): acc_o_l[i, j] *= scores_scale_0[i] for i in T.Parallel(block_H): logsum_0[i] = logsum_0[i] * scores_scale_0[i] + scores_sum_0[i] # Step 6. T.gemm(acc_s_0_cast, KV_shared_0_l, acc_o_l) T.barrier_arrive(score_max_0_ready_barrier) T.barrier_wait(scale_1_ready_barrier, k % 2) if k < loop_range - 1: T.copy( KV[bid, (2 * k + 2) * block_N:(2 * k + 3) * block_N, cur_kv_head, :h_dim], KV_shared_0_l) T.barrier_arrive(kv_shared_0_l_is_ready) # Step 11. for i, j in T.Parallel(block_H, block_N): SP0_shared[i, j] = acc_s_0[i, j] * scores_scale_1[i] T.barrier_arrive(p0_1_1_ready_barrier) # Step 13. for i, j in T.Parallel(block_H, h_dim): acc_o_l[i, j] *= scores_scale_1[i] for i in T.Parallel(block_H): logsum_0[i] = logsum_0[i] * scores_scale_1[i] T.barrier_wait(s_shared_ready_barrier, k % 2) # Step 14. T.gemm(SP1_shared, KV_shared_1_l, acc_o_l) if k < loop_range - 1: T.copy( KV[bid, (2 * k + 3) * block_N:(2 * k + 4) * block_N, cur_kv_head, :h_dim], KV_shared_1_l) T.barrier_arrive(kv_shared_1_l_is_ready) T.copy( K_pe[bid, (2 * k + 3) * block_N:(2 * k + 4) * block_N, cur_kv_head, :], K_pe_shared_1) T.barrier_arrive(kv_shared_1_pe_is_ready) T.copy(logsum_0, logsum) T.barrier_arrive(lse_0_ready_barrier) T.barrier_wait(lse_1_ready_barrier, 0) for i, j in T.Parallel(block_H, h_dim): acc_o_l[i, j] /= logsum[i] T.copy(acc_o_l, O_shared_l) T.copy(O_shared_l, Output[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :h_dim]) else: T.copy(Q_pe_shared, Q_pe_local_1) T.fill(acc_o_r, 0) T.fill(logsum_1, 0) T.copy(KV[bid, :block_N, cur_kv_head, :h_dim], KV_shared_0_l) T.barrier_arrive(kv_shared_0_l_is_ready) T.copy(KV[bid, :block_N, cur_kv_head, h_dim:], KV_shared_0_r) T.barrier_arrive(kv_shared_0_r_is_ready) T.copy(K_pe[bid, :block_N, cur_kv_head, :], K_pe_shared_0) T.barrier_arrive(kv_shared_0_pe_is_ready) for k in T.serial(loop_range): # Step 2. T.barrier_wait(kv_shared_1_l_is_ready, k % 2) T.gemm( Q_shared_l, KV_shared_1_l, acc_s_1, transpose_B=True, clear_accum=True, wg_wait=-1) T.barrier_wait(kv_shared_1_r_is_ready, k % 2) T.gemm(Q_shared_r, KV_shared_1_r, acc_s_1, transpose_B=True, wg_wait=-1) T.barrier_wait(kv_shared_1_pe_is_ready, k % 2) T.gemm(Q_pe_local_1, K_pe_shared_1, acc_s_1, transpose_B=True, wg_wait=-1) T.wait_wgmma(0) # Step 7. T.barrier_wait(score_max_0_ready_barrier, k % 2) T.copy(scores_max, scores_max_prev_1) T.fill(scores_max_1, -T.infinity(accum_dtype)) T.reduce_max(acc_s_1, scores_max_1, dim=1, clear=False) T.copy(scores_max_1, scores_max) for i in T.Parallel(block_H): scores_scale_1[i] = T.exp2(scores_max_prev_1[i] * scale - scores_max[i] * scale) # Step 8. for i, j in T.Parallel(block_H, block_N): acc_s_1[i, j] = T.exp2(acc_s_1[i, j] * scale - scores_max[i] * scale) # Step 9. T.reduce_sum(acc_s_1, scores_sum_1, dim=1) for i, j in T.Parallel(block_H, h_dim): acc_o_r[i, j] = acc_o_r[i, j] * (scores_scale_0[i] * scores_scale_1[i]) for i in T.Parallel(block_H): logsum_1[i] = logsum_1[i] * scores_scale_1[i] * scores_scale_0[ i] + scores_sum_1[i] T.barrier_arrive(scale_1_ready_barrier) # Step 10. compute O1 with KV_shared_1_rd T.copy(acc_s_1, acc_s_1_cast) T.gemm(acc_s_1_cast, KV_shared_1_r, acc_o_r, wg_wait=-1) T.copy(acc_s_1_cast, SP1_shared) T.barrier_arrive(s_shared_ready_barrier) if k < loop_range - 1: T.copy( KV[bid, (2 * k + 3) * block_N:(2 * k + 4) * block_N, cur_kv_head, h_dim:], KV_shared_1_r) T.barrier_arrive(kv_shared_1_r_is_ready) T.barrier_wait(p0_1_1_ready_barrier, k % 2) # Step 12. T.gemm(SP0_shared, KV_shared_0_r, acc_o_r) if k < loop_range - 1: T.copy( KV[bid, (2 * k + 2) * block_N:(2 * k + 3) * block_N, cur_kv_head, h_dim:], KV_shared_0_r) T.barrier_arrive(kv_shared_0_r_is_ready) T.copy( K_pe[bid, (2 * k + 2) * block_N:(2 * k + 3) * block_N, cur_kv_head, :], K_pe_shared_0) T.barrier_arrive(kv_shared_0_pe_is_ready) T.barrier_wait(lse_0_ready_barrier, 0) for i in T.Parallel(block_H): logsum[i] += logsum_1[i] T.barrier_arrive(lse_1_ready_barrier) for i, j in T.Parallel(block_H, h_dim): acc_o_r[i, j] /= logsum[i] T.copy(acc_o_r, O_shared_r) T.copy(O_shared_r, Output[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, h_dim:]) @T.prim_func def main_no_split( Q: T.Tensor([batch, heads, dim], dtype), Q_pe: T.Tensor([batch, heads, pe_dim], dtype), KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), glse: T.Tensor([batch, heads, num_split], dtype), Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), Output: T.Tensor([batch, heads, dim], dtype), ): flash_attn(Q, Q_pe, KV, K_pe, Output) return main_no_split def ref_program(q, q_pe, kv, k_pe, glse, Output_partial): # """ # Inputs: # - q (Tensor): [batch, heads, dim] # - q_pe (Tensor): [batch, heads, pe_dim] # - kv (Tensor): [batch, seqlen_kv, kv_head_num, dim] # - k_pe (Tensor): [batch, seqlen_kv, kv_head_num, pe_dim] # - glse (Tensor): [batch, heads, num_split] # - Output_partial (Tensor): [batch, heads, num_split, dim] # Outputs: # - output (Tensor): [batch, heads, dim] # """ dim = q.shape[-1] pe_dim = q_pe.shape[-1] num_head_groups = q.shape[1] // kv.shape[2] scale = (dim + pe_dim)**0.5 q = rearrange( q, 'b (h g) d -> b g h d', g=num_head_groups) # [batch_size, num_head_groups, groups, dim] q_pe = rearrange( q_pe, 'b (h g) d -> b g h d', g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] kv = rearrange(kv, 'b n h d -> b h n d') # [batch_size, groups, seqlen_kv, dim] k_pe = rearrange(k_pe, 'b n h d -> b h n d') # [batch_size, num_head_groups, groups, pe_dim] query = torch.concat([q, q_pe], dim=-1) key = torch.concat([kv, k_pe], dim=-1) scores = einsum( query, key, 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, groups, seqlen_kv] attention = F.softmax( scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] out = einsum(attention, kv, 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, groups, dim] out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] return out def main(batch=1, heads=64, kv_heads=1, kv_ctx=1024, dim=512, pe_dim=64): qk_flops = 2 * batch * heads * kv_ctx * (dim + pe_dim) pv_flops = 2 * batch * heads * kv_ctx * dim total_flops = qk_flops + pv_flops BLOCK_N = 64 BLOCK_H = 64 num_split = 1 kernel = flashattn(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H, num_split) print(kernel.get_kernel_source()) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn) profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01) latency = profiler.do_bench(warmup=500) print(f"Latency: {latency} ms") print(f"TFlops: {total_flops / latency * 1e-9} TFlops") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--batch', type=int, default=1, help='batch size') parser.add_argument('--heads', type=int, default=128, help='q heads number') parser.add_argument('--kv_heads', type=int, default=1, help='kv heads number') parser.add_argument('--kv_ctx', type=int, default=8192, help='kv context length') parser.add_argument('--dim', type=int, default=512, help='head dim') parser.add_argument('--pe_dim', type=int, default=64, help='pe head dim') args = parser.parse_args() batch, heads, kv_heads, kv_ctx, dim, pe_dim = args.batch, args.heads, args.kv_heads, args.kv_ctx, args.dim, args.pe_dim main(batch, heads, kv_heads, kv_ctx, dim, pe_dim)