test_low_latency.py

import random
import torch
import torch.distributed as dist
from functools import partial

import deep_ep
from utils import init_dist, bench, bench_kineto, calc_diff, hash_tensor, per_token_cast_back


def test_main(num_tokens: int, hidden: int, num_experts: int, num_topk: int,
              rank: int, num_ranks: int, group: dist.ProcessGroup, buffer: deep_ep.Buffer, seed: int = 0):
    torch.manual_seed(seed + rank)
    random.seed(seed + rank)

    assert num_experts % num_ranks == 0
    num_local_experts = num_experts // num_ranks

    # NOTES: the integers greater than 256 exceeds the BF16 precision limit
    rank_offset = 128
    assert num_ranks - rank_offset < 257, 'Too many ranks (exceeding test precision limit)'

    x = torch.ones((num_tokens, hidden), dtype=torch.bfloat16, device='cuda') * (rank - rank_offset)
    x[:, -128:] = torch.arange(num_tokens, device='cuda').to(torch.bfloat16).view(-1, 1)
    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=True)[1]
    topk_weights = torch.randn((num_tokens, num_topk), dtype=torch.float32, device='cuda').abs()
    # Randomly mask some positions
    for i in range(10):
        topk_idx[random.randint(0, num_tokens - 1), random.randint(0, num_topk - 1)] = -1

    # Check dispatch correctness
    do_check = True
    hash_value, num_times = 0, 0

    for return_recv_hook in (False, True):
        for dispatch_use_fp8 in (False, True):
            num_times += 1
            for i in range((num_times % 2) + 1):
                packed_recv_x, packed_recv_count, handle, event, hook = \
                    buffer.low_latency_dispatch(x, topk_idx, num_tokens, num_experts, use_fp8=dispatch_use_fp8,
                                                async_finish=not return_recv_hook, return_recv_hook=return_recv_hook)
                hook() if return_recv_hook else event.current_stream_wait()

                # print('run {}/{}, dispatch_use_fp8={}'.format(i + 1, num_times, dispatch_use_fp8))
                # return
            packed_recv_x = (packed_recv_x[0], packed_recv_x[1].contiguous()) if dispatch_use_fp8 else packed_recv_x
            simulated_gemm_x = per_token_cast_back(packed_recv_x[0].view(-1, hidden), packed_recv_x[1].view(-1, hidden // 128)).view(packed_recv_x[0].shape) \
                if dispatch_use_fp8 else packed_recv_x.clone()
            # print(f"rank{rank}: packed_recv_x[0]\n{packed_recv_x[0].cpu()}\n")
            # print(f"rank{rank}: packed_recv_x[1]\n{packed_recv_x[1].cpu()}\n")
            # print(f"simulated_gemm_x{simulated_gemm_x.cpu()}")
            all_topk_idx = torch.empty((num_ranks, num_tokens, num_topk), dtype=topk_idx.dtype, device='cuda')
            dist.all_gather_into_tensor(all_topk_idx, topk_idx, group=group)
            for i in range(num_local_experts if do_check else 0):
                expert_id = rank * num_local_experts + i
                recv_x = per_token_cast_back(packed_recv_x[0][i], packed_recv_x[1][i]) if dispatch_use_fp8 else packed_recv_x[i]
                recv_count, recv_src_info, recv_layout_range = packed_recv_count[i], handle[0][i], handle[1][i]

                # Check expert indices
                int_mask = (2 ** 32) - 1
                num_valid_tokens = recv_count.item()
                assert num_valid_tokens == (recv_layout_range & int_mask).sum().item(), f'{num_valid_tokens} != {recv_layout_range & int_mask}.sum().item()'
                assert num_valid_tokens == (all_topk_idx == expert_id).sum().item(), f'{num_valid_tokens} != {(all_topk_idx == expert_id).sum().item()}'

                # Check received data
                recv_x = recv_x[:num_valid_tokens]
                recv_x_amin = recv_x[:, :-128].amin(dim=-1)
                recv_src_info = recv_src_info[:num_valid_tokens]
                assert torch.equal(recv_x_amin, recv_x[:, :-128].amax(dim=-1))
                assert (recv_x[:, -128:] - recv_src_info.view(-1, 1) % num_tokens).sum().item() == 0
                for j in range(num_ranks):
                    begin_idx, count = (recv_layout_range[j] >> 32).item(), (recv_layout_range[j] & int_mask).item()
                    assert (recv_x_amin == j - rank_offset).sum().item() == (all_topk_idx[j] == expert_id).sum().item()
                    assert (recv_x[begin_idx:begin_idx + count][:-128] - j).sum().item() == 0
                if dispatch_use_fp8:
                    hash_value ^= hash_tensor(packed_recv_x[0][i, :num_valid_tokens])
                    hash_value ^= hash_tensor(packed_recv_x[1][i, :num_valid_tokens])
                else:
                    hash_value ^= hash_tensor(packed_recv_x[i, :num_valid_tokens])

            # Check combine correctness
            for zero_copy in (False, True):
                if zero_copy:
                    buffer.get_next_low_latency_combine_buffer(handle)[:, :, :] = simulated_gemm_x
                out = torch.empty((num_tokens, hidden), dtype=torch.bfloat16, device='cuda')
                combined_x, event, hook = buffer.low_latency_combine(simulated_gemm_x, topk_idx, topk_weights, handle,
                                                                     async_finish=not return_recv_hook,
                                                                     return_recv_hook=return_recv_hook, out=out)
                hook() if return_recv_hook else event.current_stream_wait()
                if do_check:
                    diff = calc_diff(x * topk_weights.masked_fill(topk_idx == -1, 0).sum(dim=1).view(-1, 1), combined_x)
                    assert torch.isnan(combined_x).sum().item() == 0
                    assert diff < 1e-5, f'Error: diff={diff}'
                    hash_value ^= hash_tensor(combined_x)

    # noinspection PyShadowingNames
    def large_gemm_with_hook(hook):
        mat_0 = torch.randn((8192, 8192), dtype=torch.float)
        mat_1 = torch.randn((8192, 8192), dtype=torch.float)
        mat_0 @ mat_1
        hook()

    # noinspection PyShadowingNames
    def test_func(zero_copy: bool, return_recv_hook: bool):
        recv_x, recv_count, handle, event, hook = \
            buffer.low_latency_dispatch(x, topk_idx, num_tokens, num_experts,
                                        async_finish=False, return_recv_hook=return_recv_hook)
        large_gemm_with_hook(hook) if return_recv_hook else None
        if zero_copy:
            buffer.get_next_low_latency_combine_buffer(handle)[:, :, :] = simulated_gemm_x
        combined_x, event, hook = buffer.low_latency_combine(simulated_gemm_x, topk_idx, topk_weights, handle,
                                                             zero_copy=zero_copy, return_recv_hook=return_recv_hook)
        large_gemm_with_hook(hook) if return_recv_hook else None

    # Calculate bandwidth
    num_fp8_bytes, num_bf16_bytes = (hidden + hidden / 128 * 4 + 16), hidden * 2
    num_dispatch_comm_bytes, num_combine_comm_bytes = 0, 0
    for i in range(num_tokens):
        num_selections = (topk_idx[i] != -1).sum().item()
        num_dispatch_comm_bytes += num_fp8_bytes * num_selections
        num_combine_comm_bytes += num_bf16_bytes * num_selections

    # Dispatch + combine testing
    avg_t, min_t, max_t = bench(partial(test_func, zero_copy=False, return_recv_hook=False))
    print(f'[rank {rank}] Dispatch + combine bandwidth: {(num_dispatch_comm_bytes + num_combine_comm_bytes) / 1e9 / avg_t:.2f} GB/s, '
          f'avg_t={avg_t * 1e6:.2f} us, min_t={min_t * 1e6:.2f} us, max_t={max_t * 1e6:.2f} us', flush=True)

    # Separate profiling
    for return_recv_hook in (False, True):
        group.barrier()

        dispatch_t, combine_t = bench_kineto(partial(test_func, zero_copy=True, return_recv_hook=return_recv_hook),
                                             kernel_names=('dispatch', 'combine'), barrier_comm_profiling=True,
                                             suppress_kineto_output=True)
        if not return_recv_hook:
            print(f'[rank {rank}] Dispatch bandwidth: {num_dispatch_comm_bytes / 1e9 / dispatch_t:.2f} GB/s, avg_t={dispatch_t * 1e6:.2f} us | '
                  f'Combine bandwidth: {num_combine_comm_bytes / 1e9 / combine_t:.2f} GB/s, avg_t={combine_t * 1e6:.2f} us')
        else:
            print(f'[rank {rank}] Dispatch send/recv time: {dispatch_t * 2 * 1e6:.2f} us | '
                  f'Combine send/recv time: {combine_t * 2 * 1e6:.2f} us')

    return hash_value


# noinspection PyUnboundLocalVariable
def test_loop(local_rank: int, num_local_ranks: int):
    rank, num_ranks, group = init_dist(local_rank, num_local_ranks)
    # The default setting of deepEP upstream is below: 
    num_tokens, hidden, num_topk, num_experts = 128, 7168, 8, 256

    num_rdma_bytes = deep_ep.Buffer.get_low_latency_rdma_size_hint(num_tokens, hidden, num_ranks, num_experts)
    if local_rank == 0:
        print(f'Allocating buffer size: {num_rdma_bytes / 1e6} MB ...', flush=True)
    buffer = deep_ep.Buffer(group, num_rdma_bytes=num_rdma_bytes, low_latency_mode=True,
                            num_qps_per_rank=num_experts // num_ranks, explicitly_destroy=True)
    test_main(num_tokens, hidden, num_experts, num_topk, rank, num_ranks, group, buffer, seed=1)

    do_pressure_test = False
    for seed in range(int(1e9) if do_pressure_test else 0):
        if local_rank == 0:
            print(f'Testing with seed {seed} ...', flush=True)
        ref_hash = test_main(num_tokens, hidden, num_experts, num_topk, rank, num_ranks, group, buffer, seed=seed)
        for i in range(20):
            assert test_main(num_tokens, hidden, num_experts, num_topk, rank, num_ranks, group, buffer, seed=seed) == ref_hash, f'Error: seed={seed}'

if __name__ == '__main__':
    print("main start...")
    # TODO: you may modify NUMA binding for less CPU overhead
    num_processes = 8
    torch.multiprocessing.spawn(test_loop, args=(num_processes,), nprocs=num_processes)