import os import time import torch import torch.distributed as dist # noinspection PyUnresolvedReferences import deep_ep from utils import init_dist, bench, calc_diff, inplace_unique, per_token_cast_to_fp8, per_token_cast_back # Test compatibility with low latency functions import test_low_latency def test_main(num_sms: int, local_rank: int, num_local_ranks: int, num_ranks: int, rank: int, buffer: deep_ep.Buffer, group: dist.ProcessGroup): # Settings num_tokens, hidden, num_topk, num_experts = 4096, 7168, 8, (256 // num_ranks) * num_ranks assert num_experts % num_ranks == 0 and num_local_ranks == 8 if local_rank == 0: print(f'[config] num_tokens={num_tokens}, hidden={hidden}, num_topk={num_topk}', flush=True) # Random data x = torch.ones((num_tokens, hidden), dtype=torch.bfloat16, device='cuda') * rank x_pure_rand = torch.randn((num_tokens, hidden), dtype=torch.bfloat16, device='cuda') x_e4m3 = per_token_cast_to_fp8(x) scores = torch.randn((num_tokens, num_experts), dtype=torch.float32, device='cuda').abs() + 1 topk_idx = torch.topk(scores, num_topk, dim=-1, largest=True, sorted=False)[1] topk_weights = torch.ones((num_tokens, num_topk), dtype=torch.float32, device='cuda') * rank topk_weights_pure_rand = torch.randn((num_tokens, num_topk), dtype=torch.float32, device='cuda') rank_idx = topk_idx // (num_experts // num_ranks) rank_idx.masked_fill_(topk_idx == -1, -1) inplace_unique(rank_idx, num_ranks) # Expert meta num_tokens_per_expert = torch.zeros((num_experts, ), dtype=torch.int, device='cuda') for i in range(num_experts): num_tokens_per_expert[i] = (topk_idx == i).sum() gbl_num_tokens_per_expert = num_tokens_per_expert.clone() dist.all_reduce(gbl_num_tokens_per_expert, group=group) # Rank layout meta num_tokens_per_rank = torch.empty((num_ranks, ), dtype=torch.int, device='cuda') token_idx_in_rank = torch.full((num_ranks, num_tokens), -1, dtype=torch.long, device='cuda') for i in range(num_ranks): num_tokens_per_rank[i] = (rank_idx == i).sum() token_sel = (rank_idx == i).max(dim=-1)[0] count = token_sel.sum().item() tokens = torch.sort(token_sel.to(torch.int), descending=True)[1] tokens[:count] = torch.sort(tokens[:count])[0] token_idx_in_rank[i][tokens[:count]] = torch.arange(count, dtype=torch.long, device='cuda') token_idx_in_rank = token_idx_in_rank.T.contiguous().to(torch.int) is_token_in_rank = token_idx_in_rank >= 0 gbl_num_tokens_per_rank = num_tokens_per_rank.clone() dist.all_reduce(gbl_num_tokens_per_rank, group=group) ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = \ buffer.get_dispatch_layout(topk_idx, num_experts) assert torch.allclose(ref_num_tokens_per_rank, num_tokens_per_rank) assert torch.allclose(ref_num_tokens_per_expert, num_tokens_per_expert) assert torch.allclose(ref_is_token_in_rank, is_token_in_rank) t = bench(lambda: buffer.get_dispatch_layout(topk_idx, num_experts))[0] if local_rank == 0: print(f'[layout] Kernel performance: {t * 1000:.3f} ms', flush=True) print() group.barrier() time.sleep(1) # Config nvl_buffer_size = 256 config = deep_ep.Config(num_sms, 8, nvl_buffer_size) # Test dispatch # noinspection PyShadowingNames def check_data(check_x, rank_prefix_matrix): assert torch.allclose(check_x.amin(dim=1), check_x.amax(dim=1)) check_start = 0 for i in range(num_ranks): check_end = rank_prefix_matrix[i][rank].item() assert (check_x[check_start:check_end, :].int() - i).sum().item() == 0 check_start = check_end for previous_mode in (False, True): for async_mode in (False, True): for current_x in (x_pure_rand, x, x_e4m3): for with_topk in (False, True): if local_rank == 0: print(f'[testing] Running with {"FP8" if isinstance(current_x, tuple) else "BF16"}, {"with" if with_topk else "without"} top-k (async={async_mode}, previous={previous_mode}) ...', flush=True, end='') dispatch_args = {'x': current_x, 'num_tokens_per_rank': num_tokens_per_rank, 'is_token_in_rank': is_token_in_rank, 'num_tokens_per_expert': num_tokens_per_expert, 'config': config, 'async_finish': async_mode} if with_topk: dispatch_args.update({'topk_idx': topk_idx, 'topk_weights': topk_weights_pure_rand if current_x is x_pure_rand else topk_weights}) if previous_mode: dispatch_args.update({'previous_event': buffer.capture()}) recv_x, recv_topk_idx, recv_topk_weights, recv_num_tokens_per_expert_list, handle, event = buffer.dispatch(**dispatch_args) event.current_stream_wait() if async_mode else () recv_x = per_token_cast_back(*recv_x) if isinstance(recv_x, tuple) else recv_x # Checks rank_prefix_matrix = handle[0] assert gbl_num_tokens_per_rank[rank].item() == recv_x.size(0), f'{gbl_num_tokens_per_rank[rank].item()} != {recv_x.size(0)}' assert gbl_num_tokens_per_expert.view(num_ranks, -1)[rank].tolist() == recv_num_tokens_per_expert_list if current_x is not x_pure_rand: check_data(recv_x, rank_prefix_matrix) if with_topk: # Check `topk_idx` assert (recv_topk_idx.eq(-1) | ((recv_topk_idx >= 0) & (recv_topk_idx < (num_experts // num_ranks)))).sum().item() == recv_topk_idx.numel() for i, count in enumerate(recv_num_tokens_per_expert_list): assert recv_topk_idx.eq(i).sum().item() == count # Check `topk_weights` if current_x is not x_pure_rand: recv_topk_weights[recv_topk_idx.eq(-1)] = recv_topk_weights.amax(dim=1, keepdim=True).expand_as(recv_topk_weights)[recv_topk_idx.eq(-1)] check_data(recv_topk_weights, rank_prefix_matrix) # Test cached dispatch (must without top-k staffs) # NOTES: handle must be refreshed if not with_topk: dispatch_args = {'x': current_x, 'handle': handle, 'config': config, 'async_finish': async_mode} if previous_mode: dispatch_args.update({'previous_event': buffer.capture()}) recv_x, _, _, _, _, event = buffer.dispatch(**dispatch_args) event.current_stream_wait() if async_mode else () recv_x = per_token_cast_back(*recv_x) if isinstance(recv_x, tuple) else recv_x if current_x is not x_pure_rand: check_data(recv_x, rank_prefix_matrix) # Test combine combine_args = {'x': recv_x, 'handle': handle, 'config': config, 'async_finish': async_mode} if with_topk: combine_args.update({'topk_weights': recv_topk_weights}) if previous_mode: dispatch_args.update({'previous_event': buffer.capture()}) combined_x, combined_topk_weights, event = buffer.combine(**combine_args) event.current_stream_wait() if async_mode else () check_x = combined_x.float() / is_token_in_rank.sum(dim=1).unsqueeze(1) ref_x = x_pure_rand if current_x is x_pure_rand else x assert calc_diff(check_x, ref_x) < 5e-6 if with_topk: check_topk_weights = combined_topk_weights if (current_x is x_pure_rand) else (combined_topk_weights / is_token_in_rank.sum(dim=1).unsqueeze(1)) ref_topk_weights = topk_weights_pure_rand if current_x is x_pure_rand else topk_weights assert calc_diff(check_topk_weights, ref_topk_weights) < 1e-9 # For later tuning dispatch_bf16_nvl_recv_bytes = recv_x.numel() * 2 combine_bf16_nvl_send_bytes = dispatch_bf16_nvl_recv_bytes if local_rank == 0: print(' passed', flush=True) if local_rank == 0: print() # Tune dispatch performance best_dispatch_results = None fp8_factor = (1 + 4 / 128) / 2 for current_x in (x_e4m3, x): best_time, best_results = 1e10, None nvl_recv_bytes = (dispatch_bf16_nvl_recv_bytes * fp8_factor) if isinstance(current_x, tuple) else dispatch_bf16_nvl_recv_bytes for nvl_chunk_size in range(4, 33, 4): config = deep_ep.Config(num_sms, nvl_chunk_size, nvl_buffer_size) tune_args = {'x': current_x, 'handle': handle, 'config': config} t = bench(lambda: buffer.dispatch(**tune_args))[0] if t < best_time: best_time, best_results = t, (num_sms, nvl_chunk_size) if local_rank == 0: print(f'[tuning] SMs {num_sms}, NVL chunk {nvl_chunk_size}: {nvl_recv_bytes / 1e9 / t:.2f} GB/s (NVL) ') if local_rank == 0: print(f'[tuning] Best dispatch ({"FP8" if isinstance(current_x, tuple) else "BF16"}): SMs {best_results[0]}, NVL chunk {best_results[1]}, {nvl_recv_bytes / 1e9 / best_time:.2f} GB/s (NVL)') print() if isinstance(current_x, tuple): # Gather FP8 the best config from rank 0 best_dispatch_results = torch.tensor([best_results[0], best_results[1]], dtype=torch.int32, device='cuda') all_best_fp8_results_list = [torch.zeros_like(best_dispatch_results) for _ in range(torch.distributed.get_world_size())] dist.all_gather(all_best_fp8_results_list, best_dispatch_results, group=group) best_dispatch_results = all_best_fp8_results_list[0].tolist() dispatch_config = deep_ep.Config(best_dispatch_results[0], best_dispatch_results[1], nvl_buffer_size) dispatch_args = {'x': x, 'num_tokens_per_rank': num_tokens_per_rank, 'is_token_in_rank': is_token_in_rank, 'num_tokens_per_expert': num_tokens_per_expert, 'config': dispatch_config if dispatch_config is not None else config} recv_x, _, _, _, handle, _ = buffer.dispatch(**dispatch_args) # Tune combine performance best_time, best_results = 1e10, None for nvl_chunk_size in range(1, 5, 1): config = deep_ep.Config(num_sms, nvl_chunk_size, nvl_buffer_size) tune_args = {'x': recv_x, 'handle': handle, 'config': config} t = bench(lambda: buffer.combine(**tune_args))[0] if local_rank == 0: print(f'[tuning] SMs {num_sms}, NVL chunk {nvl_chunk_size}: {combine_bf16_nvl_send_bytes / 1e9 / t:.2f} GB/s (NVL) ') if t < best_time: best_time, best_results = t, (num_sms, nvl_chunk_size) if local_rank == 0: print(f'[tuning] Best combine: SMs {best_results[0]}, NVL chunk {best_results[1]}: {combine_bf16_nvl_send_bytes / 1e9 / best_time:.2f} GB/s (NVL)') print() # noinspection PyUnboundLocalVariable def test_loop(local_rank: int, num_local_ranks: int): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) test_ll_compatibility, num_rdma_bytes = False, 0 if test_ll_compatibility: ll_num_tokens, ll_hidden, ll_num_experts, ll_num_topk = 16, 5120, 256, 9 num_rdma_bytes = deep_ep.Buffer.get_low_latency_rdma_size_hint(ll_num_tokens, ll_hidden, num_ranks, ll_num_experts) buffer = deep_ep.Buffer(group, int(1e9), num_rdma_bytes, low_latency_mode=test_ll_compatibility, num_qps_per_rank=(ll_num_experts // num_ranks if test_ll_compatibility else 1)) torch.manual_seed(rank) for i in (24, ): test_main(i, local_rank, num_local_ranks, num_ranks, rank, buffer, group) if local_rank == 0: print() # Test compatibility with low latency functions if test_ll_compatibility: buffer.clean_low_latency_buffer(ll_num_tokens, ll_hidden, ll_num_experts) test_low_latency.test_main(ll_num_tokens, ll_hidden, ll_num_experts, ll_num_topk, rank, num_ranks, group, buffer, seed=1) if __name__ == '__main__': num_processes = 8 torch.multiprocessing.spawn(test_loop, args=(num_processes, ), nprocs=num_processes)