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], pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, }, compile_flags=[ "-O3", "-Wno-deprecated-declarations", "-U__CUDA_NO_HALF_OPERATORS__", "-U__CUDA_NO_HALF_CONVERSIONS__", "-U__CUDA_NO_HALF2_OPERATORS__", "-U__CUDA_NO_BFLOAT16_CONVERSIONS__", "--expt-relaxed-constexpr", "--expt-extended-lambda", "--ptxas-options=-v,--register-usage-level=10", "-DNDEBUG" ], ) def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split, softmax_scale): sm_scale = float(softmax_scale * 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" @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=384) as (hid, bid): Q_shared_l = T.alloc_shared([block_H, dim // 2], dtype) Q_shared_r = T.alloc_shared([block_H, dim // 2], dtype) Q_tail_shared = T.alloc_shared([block_H, pe_dim], dtype) KV_shared_0_l = T.alloc_shared([block_N, dim // 2], dtype) KV_shared_0_r = T.alloc_shared([block_N, dim // 2], dtype) KV_shared_1_l = T.alloc_shared([block_N, dim // 2], dtype) KV_shared_1_r = T.alloc_shared([block_N, dim // 2], dtype) K_tail_shared_0 = T.alloc_shared([block_N, pe_dim], dtype) K_tail_shared_1 = T.alloc_shared([block_N, pe_dim], dtype) O_shared_l = Q_shared_l O_shared_r = Q_shared_r acc_o_l = T.alloc_fragment([block_H, dim // 2], accum_dtype) acc_o_r = T.alloc_fragment([block_H, dim // 2], accum_dtype) acc_s = T.alloc_fragment([block_H, block_N], accum_dtype) S_shared = T.alloc_shared([block_H, block_N], dtype) sumexp = T.alloc_fragment([block_H], accum_dtype) sum_exp_shared = T.alloc_shared([block_H], accum_dtype) sumexp_i = T.alloc_fragment([block_H], accum_dtype) alpha_shared = T.alloc_shared([block_H], accum_dtype, scope="shared") alpha_local = T.alloc_fragment([block_H], accum_dtype) m_i = T.alloc_fragment([block_H], accum_dtype) m_i_prev = T.alloc_fragment([block_H], accum_dtype) # TODO: Multi buffer bar_q = T.alloc_barrier(arrive_count=384) bar_k_0_ready = T.alloc_barrier(arrive_count=128) bar_k_1_ready = T.alloc_barrier(arrive_count=128) bar_k_0_free = T.alloc_barrier(arrive_count=256) bar_k_1_free = T.alloc_barrier(arrive_count=256) bar_sScale_and_sS_ready = T.alloc_barrier(arrive_count=256) bar_sScale_and_sS_free = T.alloc_barrier(arrive_count=256) cur_kv_head = hid // (kv_group_num // block_H) NI = T.ceildiv((seqlen_kv // num_split), block_N) tx = T.get_thread_binding() T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, 0:dim // 2], Q_shared_l) T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, dim // 2:dim], Q_shared_r) T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_tail_shared) T.barrier_arrive(bar_q) if tx < 128: T.set_max_nreg(240, 1) T.fill(sumexp, 0) T.fill(m_i, -2**30) # avoid -inf - inf to cause nan T.fill(acc_o_l, 0) T.barrier_wait(bar_q, 0) for i_i in T.serial(T.ceildiv(NI, 2)): # Buffer 0 T.barrier_wait(bar_k_0_ready[0], (i_i & 1)) T.clear(acc_s) T.gemm(Q_shared_l, KV_shared_0_l, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_shared_r, KV_shared_0_r, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_tail_shared, K_tail_shared_0, acc_s, transpose_B=True, wg_wait=-1) T.wait_wgmma(0) if i_i != 0: T.barrier_arrive(bar_sScale_and_sS_free) T.barrier_wait(bar_sScale_and_sS_free, ((i_i * 2) & 1) ^ 1) T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) for h_i in T.Parallel(block_H): alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(block_H, block_N): acc_s[h_i, bi_i] = T.exp2(acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale) T.reduce_sum(acc_s, sumexp_i, dim=1) # is this a accumulate operator? for h_i in T.Parallel(block_H): sumexp[h_i] = sumexp[h_i] * alpha_local[h_i] + sumexp_i[h_i] for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_l[h_i, d_i] *= alpha_local[h_i] T.copy(alpha_local, alpha_shared) T.copy(acc_s, S_shared) T.gemm(S_shared, KV_shared_0_l, acc_o_l) T.barrier_arrive(bar_sScale_and_sS_ready) T.barrier_arrive(bar_k_0_free[0]) # Buffer 1 T.barrier_wait(bar_k_1_ready[0], (i_i & 1)) T.clear(acc_s) T.gemm(Q_shared_l, KV_shared_1_l, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_shared_r, KV_shared_1_r, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_tail_shared, K_tail_shared_1, acc_s, transpose_B=True, wg_wait=-1) T.wait_wgmma(0) T.barrier_arrive(bar_sScale_and_sS_free) T.barrier_wait(bar_sScale_and_sS_free, ((i_i * 2 + 1) & 1) ^ 1) T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) for h_i in T.Parallel(block_H): alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(block_H, block_N): acc_s[h_i, bi_i] = T.exp2(acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale) T.reduce_sum(acc_s, sumexp_i, dim=1) # is this a accumulate operator? for h_i in T.Parallel(block_H): sumexp[h_i] = sumexp[h_i] * alpha_local[h_i] + sumexp_i[h_i] for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_l[h_i, d_i] *= alpha_local[h_i] T.copy(alpha_local, alpha_shared) T.copy(acc_s, S_shared) T.gemm(S_shared, KV_shared_1_l, acc_o_l) T.barrier_arrive(bar_sScale_and_sS_ready) T.barrier_arrive(bar_k_1_free[0]) # Rescale for h_i in T.Parallel(block_H): sum_exp_shared[h_i] = sumexp[h_i] for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_l[h_i, d_i] /= sumexp[h_i] for h_i in T.Parallel(block_H): sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * sm_scale T.copy(acc_o_l, O_shared_l) T.copy(O_shared_l, Output[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, 0:dim // 2]) elif tx >= 128 and tx < 256: T.set_max_nreg(168, 1) T.fill(acc_o_r, 0) for i_i in T.serial(T.ceildiv(NI, 2)): # Buffer 0 T.barrier_arrive(bar_sScale_and_sS_ready) T.barrier_wait(bar_sScale_and_sS_ready, ((i_i * 2) & 1)) for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_r[h_i, d_i] *= alpha_shared[h_i] T.gemm(S_shared, KV_shared_0_r, acc_o_r) T.barrier_arrive(bar_k_0_free[0]) T.barrier_arrive(bar_sScale_and_sS_free) # Buffer 1 T.barrier_arrive(bar_sScale_and_sS_ready) T.barrier_wait(bar_sScale_and_sS_ready, ((i_i * 2 + 1) & 1)) for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_r[h_i, d_i] *= alpha_shared[h_i] T.gemm(S_shared, KV_shared_1_r, acc_o_r) T.barrier_arrive(bar_k_1_free[0]) if i_i != T.ceildiv(NI, 2) - 1: T.barrier_arrive(bar_sScale_and_sS_free) # Rescale for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_r[h_i, d_i] /= sum_exp_shared[h_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, dim // 2:dim]) elif tx >= 256: # producer T.set_max_nreg(80, 0) for i_i in T.serial(T.ceildiv(NI, 2)): # Buffer 0 T.barrier_wait(bar_k_0_free[0], ((i_i & 1) ^ 1)) for r in T.serial(4): kv_indices = (i_i * 2) * block_N + r * 16 + (tx - 256) // 8 with T.attr("default", "async_scope", 1): for u in T.serial(4): for v in T.vectorized(8): KV_shared_0_l[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[bid, kv_indices, cur_kv_head, 64 * u + (tx - 256) % 8 * 8 + v] KV_shared_0_r[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[bid, kv_indices, cur_kv_head, dim // 2 + 64 * u + (tx - 256) % 8 * 8 + v] with T.attr("default", "async_scope", 1): for v in T.vectorized(8): K_tail_shared_0[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + v] = K_pe[bid, kv_indices, cur_kv_head, (tx - 256) % 8 * 8 + v] T.cp_async_barrier_noinc(bar_k_0_ready[0]) # Buffer 1 T.barrier_wait(bar_k_1_free[0], ((i_i & 1) ^ 1)) for r in T.serial(4): kv_indices = (i_i * 2 + 1) * block_N + r * 16 + (tx - 256) // 8 with T.attr("default", "async_scope", 1): for u in T.serial(4): for v in T.vectorized(8): KV_shared_1_l[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[bid, kv_indices, cur_kv_head, 64 * u + (tx - 256) % 8 * 8 + v] KV_shared_1_r[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[bid, kv_indices, cur_kv_head, dim // 2 + 64 * u + (tx - 256) % 8 * 8 + v] with T.attr("default", "async_scope", 1): for v in T.vectorized(8): K_tail_shared_1[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + v] = K_pe[bid, kv_indices, cur_kv_head, (tx - 256) % 8 * 8 + v] T.cp_async_barrier_noinc(bar_k_1_ready[0]) @T.macro def flash_attn_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), ): with T.Kernel( batch, heads // min(block_H, kv_group_num), num_split, threads=384) as (bid, hid, bz): Q_shared_l = T.alloc_shared([block_H, dim // 2], dtype) Q_shared_r = T.alloc_shared([block_H, dim // 2], dtype) Q_tail_shared = T.alloc_shared([block_H, pe_dim], dtype) KV_shared_0_l = T.alloc_shared([block_N, dim // 2], dtype) KV_shared_0_r = T.alloc_shared([block_N, dim // 2], dtype) KV_shared_1_l = T.alloc_shared([block_N, dim // 2], dtype) KV_shared_1_r = T.alloc_shared([block_N, dim // 2], dtype) K_tail_shared_0 = T.alloc_shared([block_N, pe_dim], dtype) K_tail_shared_1 = T.alloc_shared([block_N, pe_dim], dtype) O_shared_l = Q_shared_l O_shared_r = Q_shared_r acc_o_l = T.alloc_fragment([block_H, dim // 2], accum_dtype) acc_o_r = T.alloc_fragment([block_H, dim // 2], accum_dtype) acc_s = T.alloc_fragment([block_H, block_N], accum_dtype) S_shared = T.alloc_shared([block_H, block_N], dtype) sumexp = T.alloc_fragment([block_H], accum_dtype) sum_exp_shared = T.alloc_shared([block_H], accum_dtype) sumexp_i = T.alloc_fragment([block_H], accum_dtype) alpha_shared = T.alloc_shared([block_H], accum_dtype, scope="shared") alpha_local = T.alloc_fragment([block_H], accum_dtype) m_i = T.alloc_fragment([block_H], accum_dtype) m_i_prev = T.alloc_fragment([block_H], accum_dtype) # TODO: Multi buffer bar_q = T.alloc_barrier(arrive_count=384) bar_k_0_ready = T.alloc_barrier(arrive_count=128) bar_k_1_ready = T.alloc_barrier(arrive_count=128) bar_k_0_free = T.alloc_barrier(arrive_count=256) bar_k_1_free = T.alloc_barrier(arrive_count=256) bar_sScale_and_sS_ready = T.alloc_barrier(arrive_count=256) bar_sScale_and_sS_free = T.alloc_barrier(arrive_count=256) cur_kv_head = hid // (kv_group_num // block_H) NI = T.ceildiv((seqlen_kv // num_split), block_N) tx = T.get_thread_binding() T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, 0:dim // 2], Q_shared_l) T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, dim // 2:dim], Q_shared_r) T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_tail_shared) T.barrier_arrive(bar_q) if tx < 128: T.set_max_nreg(240, 1) T.fill(sumexp, 0) T.fill(m_i, -2**30) # avoid -inf - inf to cause nan T.fill(acc_o_l, 0) T.barrier_wait(bar_q, 0) for i_i in T.serial(T.ceildiv(NI, 2)): # Buffer 0 T.barrier_wait(bar_k_0_ready[0], (i_i & 1)) T.clear(acc_s) T.gemm(Q_shared_l, KV_shared_0_l, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_shared_r, KV_shared_0_r, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_tail_shared, K_tail_shared_0, acc_s, transpose_B=True, wg_wait=-1) T.wait_wgmma(0) if i_i != 0: T.barrier_arrive(bar_sScale_and_sS_free) T.barrier_wait(bar_sScale_and_sS_free, ((i_i * 2) & 1) ^ 1) T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) for h_i in T.Parallel(block_H): alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(block_H, block_N): acc_s[h_i, bi_i] = T.exp2(acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale) T.reduce_sum(acc_s, sumexp_i, dim=1) # is this a accumulate operator? for h_i in T.Parallel(block_H): sumexp[h_i] = sumexp[h_i] * alpha_local[h_i] + sumexp_i[h_i] for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_l[h_i, d_i] *= alpha_local[h_i] T.copy(alpha_local, alpha_shared) T.copy(acc_s, S_shared) T.gemm(S_shared, KV_shared_0_l, acc_o_l) T.barrier_arrive(bar_sScale_and_sS_ready) T.barrier_arrive(bar_k_0_free[0]) # Buffer 1 T.barrier_wait(bar_k_1_ready[0], (i_i & 1)) T.clear(acc_s) T.gemm(Q_shared_l, KV_shared_1_l, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_shared_r, KV_shared_1_r, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_tail_shared, K_tail_shared_1, acc_s, transpose_B=True, wg_wait=-1) T.wait_wgmma(0) T.barrier_arrive(bar_sScale_and_sS_free) T.barrier_wait(bar_sScale_and_sS_free, ((i_i * 2 + 1) & 1) ^ 1) T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) for h_i in T.Parallel(block_H): alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(block_H, block_N): acc_s[h_i, bi_i] = T.exp2(acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale) T.reduce_sum(acc_s, sumexp_i, dim=1) # is this a accumulate operator? for h_i in T.Parallel(block_H): sumexp[h_i] = sumexp[h_i] * alpha_local[h_i] + sumexp_i[h_i] for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_l[h_i, d_i] *= alpha_local[h_i] T.copy(alpha_local, alpha_shared) T.copy(acc_s, S_shared) T.gemm(S_shared, KV_shared_1_l, acc_o_l) T.barrier_arrive(bar_sScale_and_sS_ready) T.barrier_arrive(bar_k_1_free[0]) # Rescale for h_i in T.Parallel(block_H): sum_exp_shared[h_i] = sumexp[h_i] for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_l[h_i, d_i] /= sumexp[h_i] for h_i in T.Parallel(block_H): sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * sm_scale T.copy(acc_o_l, O_shared_l) T.copy( O_shared_l, Output_partial[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, bz, 0:dim // 2]) T.copy(sumexp, glse[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, bz]) elif tx >= 128 and tx < 256: T.set_max_nreg(168, 1) T.fill(acc_o_r, 0) for i_i in T.serial(T.ceildiv(NI, 2)): # Buffer 0 T.barrier_arrive(bar_sScale_and_sS_ready) T.barrier_wait(bar_sScale_and_sS_ready, ((i_i * 2) & 1)) for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_r[h_i, d_i] *= alpha_shared[h_i] T.gemm(S_shared, KV_shared_0_r, acc_o_r) T.barrier_arrive(bar_k_0_free[0]) T.barrier_arrive(bar_sScale_and_sS_free) # Buffer 1 T.barrier_arrive(bar_sScale_and_sS_ready) T.barrier_wait(bar_sScale_and_sS_ready, ((i_i * 2 + 1) & 1)) for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_r[h_i, d_i] *= alpha_shared[h_i] T.gemm(S_shared, KV_shared_1_r, acc_o_r) T.barrier_arrive(bar_k_1_free[0]) if i_i != T.ceildiv(NI, 2) - 1: T.barrier_arrive(bar_sScale_and_sS_free) # Rescale for h_i, d_i in T.Parallel(block_H, dim // 2): acc_o_r[h_i, d_i] /= sum_exp_shared[h_i] T.copy(acc_o_r, O_shared_r) T.copy( O_shared_r, Output_partial[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, bz, dim // 2:dim]) elif tx >= 256: # producer T.set_max_nreg(80, 0) for i_i in T.serial(T.ceildiv(NI, 2)): # Buffer 0 T.barrier_wait(bar_k_0_free[0], ((i_i & 1) ^ 1)) for r in T.serial(4): kv_indices = (seqlen_kv // num_split) * bz + ( i_i * 2) * block_N + r * 16 + (tx - 256) // 8 with T.attr("default", "async_scope", 1): for u in T.serial(4): for v in T.vectorized(8): KV_shared_0_l[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[bid, kv_indices, cur_kv_head, 64 * u + (tx - 256) % 8 * 8 + v] KV_shared_0_r[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[bid, kv_indices, cur_kv_head, dim // 2 + 64 * u + (tx - 256) % 8 * 8 + v] with T.attr("default", "async_scope", 1): for v in T.vectorized(8): K_tail_shared_0[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + v] = K_pe[bid, kv_indices, cur_kv_head, (tx - 256) % 8 * 8 + v] T.cp_async_barrier_noinc(bar_k_0_ready[0]) # Buffer 1 T.barrier_wait(bar_k_1_free[0], ((i_i & 1) ^ 1)) for r in T.serial(4): kv_indices = (seqlen_kv // num_split) * bz + ( i_i * 2 + 1) * block_N + r * 16 + (tx - 256) // 8 with T.attr("default", "async_scope", 1): for u in T.serial(4): for v in T.vectorized(8): KV_shared_1_l[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[bid, kv_indices, cur_kv_head, 64 * u + (tx - 256) % 8 * 8 + v] KV_shared_1_r[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[bid, kv_indices, cur_kv_head, dim // 2 + 64 * u + (tx - 256) % 8 * 8 + v] with T.attr("default", "async_scope", 1): for v in T.vectorized(8): K_tail_shared_1[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + v] = K_pe[bid, kv_indices, cur_kv_head, (tx - 256) % 8 * 8 + v] T.cp_async_barrier_noinc(bar_k_1_ready[0]) @T.macro def combine( 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), ): with T.Kernel(heads, batch, threads=128) as (hid, bz): po_local = T.alloc_fragment([dim], dtype) o_accum_local = T.alloc_fragment([dim], accum_dtype) lse_local_split = T.alloc_local([1], accum_dtype) lse_logsum_local = T.alloc_local([1], accum_dtype) lse_max_local = T.alloc_local([1], accum_dtype) scale_local = T.alloc_local([1], accum_dtype) T.annotate_layout({ lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i), }) T.clear(lse_logsum_local) T.clear(o_accum_local) lse_max_local[0] = -T.infinity(accum_dtype) for k in T.serial(num_split): lse_max_local[0] = T.max(lse_max_local[0], glse[bz, hid, k]) for k in T.Pipelined(num_split, num_stages=1): lse_local_split[0] = glse[bz, hid, k] lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0]) lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0] for k in T.serial(num_split): for i in T.Parallel(dim): po_local[i] = Output_partial[bz, hid, k, i] lse_local_split[0] = glse[bz, hid, k] scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0]) for i in T.Parallel(dim): o_accum_local[i] += po_local[i] * scale_local[0] for i in T.Parallel(dim): Output[bz, hid, i] = o_accum_local[i] @T.prim_func def main_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_split(Q, Q_pe, KV, K_pe, glse, Output_partial) combine(glse, Output_partial, Output) @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) if num_split > 1: return main_split else: 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=128, kv_heads=1, kv_ctx=8192, 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 = min(64, heads // kv_heads) num_split = 1 softmax_scale = (dim + pe_dim)**-0.5 kernel = flashattn(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H, num_split, softmax_scale) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn) profiler.assert_allclose(ref_program, rtol=1e-4, atol=1e-4) 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=132, 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)