import torch import torch.nn.functional as F import tilelang from tilelang.autotuner import * import tilelang.language as T import itertools import argparse from functools import partial def get_configs(): iter_params = dict(block_M=[128], block_N=[128], num_stages=[2], threads=[256]) return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())] @autotune(configs=get_configs(), warmup=10, rep=10) @tilelang.jit( out_idx=[3], pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, }, ) def flashattn(batch, heads, seq_q, seq_kv, dim, is_causal, block_M=64, block_N=64, num_stages=1, threads=128): scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) q_shape = [batch, heads, seq_q, dim] kv_shape = [batch, heads, seq_kv, dim] dtype = "float16" accum_dtype = "float" past_len = seq_kv - seq_q assert past_len >= 0, "seq_kv must be greater than or equal to seq_q" @T.macro def MMA0( K: T.Tensor(kv_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): q_idx = bx * block_M + i + past_len k_idx = k * block_N + j acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) else: # We shall fill -inf for OOB positions for i, j in T.Parallel(block_M, block_N): acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_kv, -T.infinity(acc_s.dtype), 0) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) @T.macro def MMA1( V: T.Tensor(kv_shape, dtype), V_shared: T.SharedBuffer([block_N, 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) for i in T.Parallel(block_M): scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) # 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(q_shape, dtype), K: T.Tensor(kv_shape, dtype), V: T.Tensor(kv_shape, dtype), Output: T.Tensor(q_shape, dtype), ): with T.Kernel(T.ceildiv(seq_q, 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) 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)) loop_range = ( T.min(T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N)) if is_causal else T.ceildiv(seq_kv, block_N) ) for k in T.Pipelined(loop_range, num_stages=num_stages): 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 def ref_program(Q, K, V, is_causal): dim = Q.size(-1) scores = torch.einsum("bhqd,bhkd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_q = Q.size(2) seq_kv = K.size(2) mask = torch.tril(torch.ones(seq_q, seq_kv, device=scores.device), seq_kv - seq_q) mask = mask.unsqueeze(0).unsqueeze(0) scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) output = torch.einsum("bhqk,bhkd->bhqd", attention_weights, V) return output def main( batch: int = 1, heads: int = 1, seq_q: int = 256, seq_kv: int = 256, dim: int = 64, is_causal: bool = False, tune: bool = False, ): flops_per_matmul = 2.0 * batch * heads * seq_q * seq_kv * dim total_flops = 2 * flops_per_matmul if is_causal: total_flops *= 0.5 if not tune: kernel = flashattn(batch, heads, seq_q, seq_kv, dim, is_causal, block_M=64, block_N=64, num_stages=1, threads=128) ref_program_processed = partial(ref_program, is_causal=is_causal) profiler = kernel.get_profiler() profiler.assert_allclose(ref_program_processed, rtol=0.01, atol=0.01) print("All checks pass.") latency = profiler.do_bench(ref_program_processed, warmup=500) print("Ref: {:.2f} ms".format(latency)) print("Ref: {:.2f} TFlops".format(total_flops / latency * 1e-9)) latency = profiler.do_bench(warmup=500) print("Tile-lang: {:.2f} ms".format(latency)) print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) else: kernel = flashattn(batch, heads, seq_q, seq_kv, dim, is_causal) best_latency = kernel.latency best_config = kernel.config ref_latency = kernel.ref_latency print(f"Best latency: {best_latency}") print(f"Best TFlops: {total_flops / best_latency * 1e-9}") print(f"Best config: {best_config}") print(f"Ref latency: {ref_latency}") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--batch", type=int, default=1, help="batch size") parser.add_argument("--heads", type=int, default=1, help="heads") parser.add_argument("--seq_q", type=int, default=256, help="query sequence length") parser.add_argument("--seq_kv", type=int, default=256, help="key/value sequence length") parser.add_argument("--dim", type=int, default=64, help="dim") parser.add_argument("--is_causal", action="store_true", help="causal", default=False) parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.is_causal, args.tune)