test_intranode.py

import argparse
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


# noinspection PyShadowingNames
def test_main(args: argparse.Namespace, num_sms: int, local_rank: int, num_ranks: int, rank: int,
              buffer: deep_ep.Buffer, group: dist.ProcessGroup):
    # Settings
    num_tokens, hidden = args.num_tokens, args.hidden
    num_topk, num_experts = args.num_topk, args.num_experts

    assert num_experts % num_ranks == 0
    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) if deep_ep.Buffer.is_sm90_compiled() else None
    x_e4m3 = (x_e4m3[0], x_e4m3[1].T.contiguous().T) if x_e4m3 is not None else None
    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('', flush=True)
    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 filter(lambda elem: elem is not None, (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)
                    recv_topk_weights_clone = None
                    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`
                        recv_topk_weights_clone = recv_topk_weights.clone()
                        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 `num_worst_tokens != 0`
                    if with_topk:
                        num_worst_tokens = num_tokens * num_ranks
                        dispatch_args.update({'num_worst_tokens': num_worst_tokens})
                        recv_worst_x, recv_worst_topk_idx, recv_worst_topk_weights, empty_list, _, event = buffer.dispatch(**dispatch_args)
                        event.current_stream_wait() if async_mode else ()
                        recv_worst_x = per_token_cast_back(*recv_worst_x) if isinstance(recv_worst_x, tuple) else recv_worst_x
                        assert len(empty_list) == 0
                        assert num_worst_tokens == recv_worst_x.size(0)
                        assert num_worst_tokens == recv_worst_topk_idx.size(0)
                        assert num_worst_tokens == recv_worst_topk_weights.size(0)
                        assert torch.equal(recv_x, recv_worst_x[:recv_x.size(0)])
                        assert torch.equal(recv_topk_idx, recv_worst_topk_idx[:recv_x.size(0)])
                        assert torch.equal(recv_topk_weights_clone, recv_worst_topk_weights[:recv_x.size(0)])
                        assert torch.all(recv_worst_topk_idx[recv_x.size(0):] == -1).item()

                    # Test cached dispatch (must without top-k staffs)
                    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:
                        combine_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('', flush=True)

    # Tune dispatch performance
    best_dispatch_results = None
    fp8_factor = (1 + 4 / 128) / 2
    for current_x in filter(lambda elem: elem is not None, (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 tuple(range(4, 33, 2)) + (0, ):
            if nvl_chunk_size > 0:
                config = deep_ep.Config(num_sms, nvl_chunk_size, nvl_buffer_size)
            else:
                # Test default config as well
                deep_ep.Buffer.set_num_sms(num_sms)
                config = deep_ep.Buffer.get_dispatch_config(num_ranks)
            tune_args = {'x': current_x, 'handle': handle, 'config': config}
            t = bench(lambda: buffer.dispatch(**tune_args))[0]
            if t < best_time and nvl_chunk_size > 0:
                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 if nvl_chunk_size else "default"}: '
                      f'{nvl_recv_bytes / 1e9 / t:.2f} GB/s (NVL), {t * 1e6:.2f} us', flush=True)
        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), t: {best_time * 1e6:.2f} us', flush=True)
            print('', flush=True)

        # Gather the best config from rank 0 and the first test setting
        if best_dispatch_results is None:
            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 tuple(range(1, 17, 1)) + (0, ):
        if nvl_chunk_size > 0:
            config = deep_ep.Config(num_sms, nvl_chunk_size, nvl_buffer_size)
        else:
            # Test default config as well
            deep_ep.Buffer.set_num_sms(num_sms)
            config = deep_ep.Buffer.get_combine_config(num_ranks)
        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 if nvl_chunk_size else "default"}: '
                  f'{combine_bf16_nvl_send_bytes / 1e9 / t:.2f} GB/s (NVL), {t * 1e6:.2f} us', flush=True)
            if t < best_time and nvl_chunk_size > 0:
                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), t: {best_time * 1e6:.2f} us', flush=True)
        print('', flush=True)


# noinspection PyUnboundLocalVariable,PyShadowingNames
def test_loop(local_rank: int, num_local_ranks: int, args: argparse.Namespace):
    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(2e9), num_rdma_bytes, low_latency_mode=test_ll_compatibility,
                            num_qps_per_rank=(ll_num_experts // num_ranks if test_ll_compatibility else 1), explicitly_destroy=True)
    torch.manual_seed(rank)

    for i in (24, ):
        test_main(args, i, local_rank, num_ranks, rank, buffer, group)
        if local_rank == 0:
            print('', flush=True)

    # 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)

    # Destroy the buffer runtime and communication group
    buffer.destroy()
    dist.barrier()
    dist.destroy_process_group()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Test intranode EP kernels')
    parser.add_argument('--num-processes', type=int, default=8,
                       help='Number of processes to spawn (default: 8)')
    parser.add_argument('--num-tokens', type=int, default=4096,
                       help='Number of tokens (default: 4096)')
    parser.add_argument('--hidden', type=int, default=7168,
                       help='Hidden dimension size (default: 7168)')
    parser.add_argument('--num-topk', type=int, default=8,
                       help='Number of top-k experts (default: 8)')
    parser.add_argument('--num-experts', type=int, default=256,
                       help='Number of experts (default: 256)')
    args = parser.parse_args()

    num_processes = args.num_processes
    torch.multiprocessing.spawn(test_loop, args=(num_processes, args), nprocs=num_processes)