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https://github.com/microsoft/mscclpp.git
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10cd0012f1
- Report both per-rank and aggregate BW to align with NCCL-EP's ep_bench (which reports per-rank GB/s). - Accept MSCCLPP_EP_LL_TOKENS/HIDDEN/TOPK/EXPERTS_PER_RANK env overrides so we can match external benchmark problem sizes (NCCL-EP LL defaults are num_tokens=128, hidden=7168, top_k=8).
310 lines
12 KiB
Python
310 lines
12 KiB
Python
"""Multi-rank low-latency functional test for mscclpp_ep.
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Launch with (intra-node, 8 GPUs):
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torchrun --nproc_per_node=8 test/python/ext/ep/test_low_latency_multirank.py
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Launch with (2 nodes, 1 GPU per node -- DeepEP's recommended LL topology):
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# node 0:
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MASTER_ADDR=<master> MASTER_PORT=29600 NODE_RANK=0 \
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torchrun --nnodes=2 --nproc_per_node=1 --rdzv-backend=c10d \
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--rdzv-endpoint=<master>:29600 test/python/ext/ep/test_low_latency_multirank.py
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# node 1:
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MASTER_ADDR=<master> MASTER_PORT=29600 NODE_RANK=1 \
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torchrun --nnodes=2 --nproc_per_node=1 --rdzv-backend=c10d \
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--rdzv-endpoint=<master>:29600 test/python/ext/ep/test_low_latency_multirank.py
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Exercises the LL dispatch + combine round-trip on a single node. The
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minimal correctness check:
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- dispatch: per-expert received token counts agree with an all-gathered
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reference computed from topk_idx across all ranks;
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- combine: the reconstructed x matches the analytical sum
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``x * sum(topk_weights, masked by topk_idx == -1)``.
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Known limitation (see src/ext/ep/README.md): the LL kernels drive every
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peer via MSCCL++ PortChannel. Intra-node IB loopback between two HCAs on
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the same host (what an 8-GPU single-node launch exercises) currently hangs
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during dispatch; cross-node LL with one GPU per node works as designed.
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Adapted from DeepEP/tests/test_low_latency.py stripped to the bare checks
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we need for an LL port smoke test. BF16-only (no FP8 check).
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"""
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from __future__ import annotations
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import os
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import random
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import sys
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import torch
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import torch.distributed as dist
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def init_dist():
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rank = int(os.environ["RANK"])
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world_size = int(os.environ["WORLD_SIZE"])
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local_rank = int(os.environ.get("LOCAL_RANK", rank))
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torch.cuda.set_device(local_rank)
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dist.init_process_group(
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backend="nccl",
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init_method=f"tcp://{os.environ.get('MASTER_ADDR','127.0.0.1')}:{os.environ.get('MASTER_PORT','29500')}",
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world_size=world_size,
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rank=rank,
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)
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return rank, world_size, local_rank, dist.new_group(list(range(world_size)))
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def main():
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rank, num_ranks, local_rank, group = init_dist()
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from mscclpp.ext import ep
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# Shrink the "bf16 precision" anchor to keep values small.
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rank_offset = 128
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assert num_ranks - rank_offset < 257, "too many ranks for bf16 precision anchor"
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num_tokens = int(os.environ.get("MSCCLPP_EP_LL_TOKENS", "64"))
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hidden = int(os.environ.get("MSCCLPP_EP_LL_HIDDEN", "7168")) # LL kernels are compiled for a fixed set; see SWITCH_HIDDEN
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num_topk = int(os.environ.get("MSCCLPP_EP_LL_TOPK", "4"))
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num_experts_per_rank = int(os.environ.get("MSCCLPP_EP_LL_EXPERTS_PER_RANK", "4"))
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num_experts = num_ranks * num_experts_per_rank
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assert num_experts % num_ranks == 0
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num_local_experts = num_experts // num_ranks
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torch.manual_seed(0xB3C4 + rank)
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random.seed(0xB3C4 + rank)
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x = torch.ones((num_tokens, hidden), dtype=torch.bfloat16, device="cuda") * (rank - rank_offset)
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# Encode the per-token index into the last 128 elements so the receiver
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# can verify which source token it is looking at.
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x[:, -128:] = (
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torch.arange(num_tokens, device="cuda").to(torch.bfloat16).view(-1, 1)
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)
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scores = torch.randn((num_tokens, num_experts), dtype=torch.float32, device="cuda").abs() + 1
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topk_idx = torch.topk(scores, num_topk, dim=-1, largest=True, sorted=True)[1]
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topk_weights = torch.randn((num_tokens, num_topk), dtype=torch.float32, device="cuda").abs()
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# Randomly mask some positions
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for _ in range(min(10, num_tokens)):
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topk_idx[random.randint(0, num_tokens - 1), random.randint(0, num_topk - 1)] = -1
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num_rdma_bytes = ep.Buffer.get_low_latency_rdma_size_hint(
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num_tokens, hidden, num_ranks, num_experts
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)
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if rank == 0:
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print(
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f"[cfg] num_ranks={num_ranks} num_tokens={num_tokens} hidden={hidden} "
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f"num_experts={num_experts} num_topk={num_topk} "
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f"num_rdma_bytes={num_rdma_bytes}",
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flush=True,
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)
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buf = ep.Buffer(
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group,
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num_nvl_bytes=0,
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num_rdma_bytes=num_rdma_bytes,
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low_latency_mode=True,
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num_qps_per_rank=max(1, num_experts // num_ranks),
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)
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print(
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f"[rank {rank}] Buffer created is_available={buf.is_available()} "
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f"is_internode={buf.is_internode_available()}",
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flush=True,
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)
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assert buf.is_available()
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dist.barrier(group=group)
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torch.cuda.synchronize()
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print(f"[rank {rank}] pre-dispatch", flush=True)
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# --- Dispatch ---
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# Return tuple (7 items):
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# packed_recv_x, packed_recv_x_scales (optional, FP8-only),
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# packed_recv_count, packed_recv_src_info, packed_recv_layout_range,
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# event, hook
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(
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packed_recv_x, _packed_recv_x_scales,
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packed_recv_count, packed_recv_src_info, packed_recv_layout_range,
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_event, recv_hook,
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) = buf.low_latency_dispatch(
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x, topk_idx, num_tokens, num_experts,
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False, False, True, # use_fp8, async, return_recv_hook
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)
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# Send phase launched on compute_stream; wait for local launch.
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torch.cuda.synchronize()
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dist.barrier(group=group)
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print(f"[rank {rank}] dispatch-send done, calling hook", flush=True)
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recv_hook() # Recv phase.
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torch.cuda.synchronize()
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print(f"[rank {rank}] post-dispatch", flush=True)
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handle = (packed_recv_src_info, packed_recv_layout_range)
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# packed_recv_x: [num_local_experts, num_ranks * num_max_dispatch_tokens_per_rank, hidden]
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# packed_recv_count: [num_local_experts] int32
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# Reference: gather all ranks' topk_idx and count expected tokens per expert.
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all_topk_idx = torch.empty((num_ranks, num_tokens, num_topk), dtype=topk_idx.dtype, device="cuda")
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dist.all_gather_into_tensor(all_topk_idx, topk_idx, group=group)
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int_mask = (1 << 32) - 1
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for i in range(num_local_experts):
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expert_id = rank * num_local_experts + i
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recv_count = int(packed_recv_count[i].item())
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expected_count = int((all_topk_idx == expert_id).sum().item())
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recv_layout_range = handle[1][i]
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layout_sum = int((recv_layout_range & int_mask).sum().item())
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assert recv_count == expected_count, (
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f"rank{rank} expert{expert_id}: recv_count={recv_count} != expected={expected_count}"
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)
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assert layout_sum == recv_count, (
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f"rank{rank} expert{expert_id}: layout range sum {layout_sum} != recv_count {recv_count}"
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)
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if recv_count:
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recv_x = packed_recv_x[i, :recv_count]
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# All columns except the last 128 should share the value (src_rank - rank_offset)
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recv_x_lo = recv_x[:, :-128]
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amin = recv_x_lo.amin(dim=-1)
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amax = recv_x_lo.amax(dim=-1)
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assert torch.equal(amin, amax), f"rank{rank} expert{expert_id}: non-uniform recv block"
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if rank == 0:
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print(f"[dispatch] OK (ranks={num_ranks})", flush=True)
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# --- Combine ---
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# Simulate the downstream GEMM output = identity (bf16 copy) so combine
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# returns sum(x * weight) across experts.
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simulated_gemm_x = packed_recv_x.clone()
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out = torch.empty((num_tokens, hidden), dtype=torch.bfloat16, device="cuda")
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# Signature: (x, topk_idx, topk_weights, src_info, layout_range,
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# num_max_dispatch_tokens_per_rank, num_experts,
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# zero_copy, async, return_recv_hook, out)
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src_info, layout_range = handle[0], handle[1]
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combined_x, _event, _hook = buf.low_latency_combine(
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simulated_gemm_x, topk_idx, topk_weights,
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src_info, layout_range,
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num_tokens, num_experts,
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False, False, False, # zero_copy, async, return_recv_hook
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out,
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)
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# Analytical expected: each token i, weighted sum over topk entries that
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# are not -1. Every expert returns the original x[i] (since simulated
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# gemm is identity), so the combine output should be
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# x[i] * sum(topk_weights[i, j] for j where topk_idx[i,j] != -1).
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weight_sum = topk_weights.masked_fill(topk_idx == -1, 0.0).sum(dim=1).view(-1, 1)
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expected = (x.float() * weight_sum).to(torch.bfloat16)
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diff = (combined_x.float() - expected.float()).abs().max().item()
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max_exp = expected.float().abs().max().item()
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print(
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f"[combine r{rank}] max|got-expected|={diff:.4e} max|expected|={max_exp:.4e}",
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flush=True,
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)
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assert torch.isnan(combined_x).any().item() is False
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assert diff < 1e-2, f"rank{rank}: LL combine mismatch diff={diff}"
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dist.barrier(group=group)
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if rank == 0:
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print("PASS", flush=True)
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# ------------------------------------------------------------------
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# Optional benchmark (enable with MSCCLPP_EP_BENCH=1). Times dispatch
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# and combine separately, reporting per-iter latency (max across ranks)
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# and aggregate effective bandwidth (sum across ranks).
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# ------------------------------------------------------------------
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if os.environ.get("MSCCLPP_EP_BENCH", "0") != "1":
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return
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warmup = int(os.environ.get("MSCCLPP_EP_BENCH_WARMUP", "5"))
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iters = int(os.environ.get("MSCCLPP_EP_BENCH_ITERS", "20"))
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def _dispatch():
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return buf.low_latency_dispatch(
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x, topk_idx, num_tokens, num_experts,
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False, False, False, # use_fp8, async, return_recv_hook
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)
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def _combine(dout):
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(recv_x, _scales, _cnt, src_info_, layout_range_, _ev, _hk) = dout
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out_ = torch.empty((num_tokens, hidden), dtype=torch.bfloat16, device="cuda")
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buf.low_latency_combine(
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recv_x.clone(), topk_idx, topk_weights,
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src_info_, layout_range_,
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num_tokens, num_experts,
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False, False, False,
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out_,
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)
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for _ in range(warmup):
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_combine(_dispatch())
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torch.cuda.synchronize()
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dist.barrier(group=group)
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start_ev = torch.cuda.Event(enable_timing=True)
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end_ev = torch.cuda.Event(enable_timing=True)
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start_ev.record()
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dout = None
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for _ in range(iters):
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dout = _dispatch()
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end_ev.record()
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torch.cuda.synchronize()
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disp_us = start_ev.elapsed_time(end_ev) * 1e3 / iters
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recv_tokens = int(dout[2].sum().item()) # packed_recv_count summed over local experts
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dist.barrier(group=group)
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start_ev.record()
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for _ in range(iters):
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_combine(dout)
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end_ev.record()
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torch.cuda.synchronize()
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comb_us = start_ev.elapsed_time(end_ev) * 1e3 / iters
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# Dispatch payload: recv_tokens × hidden × bf16 (received on this rank).
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# Combine payload: recv_tokens × hidden × bf16 as well -- each local expert
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# sends one copy per dispatched token back to its owner, so the bytes on
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# the wire match dispatch. Using num_tokens × hidden here would under-count
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# the actual send payload by ~num_topk×.
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disp_bytes = recv_tokens * hidden * 2
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comb_bytes = recv_tokens * hidden * 2
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disp_bw = disp_bytes / (disp_us * 1e-6) / 1e9
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comb_bw = comb_bytes / (comb_us * 1e-6) / 1e9
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disp_us_t = torch.tensor([disp_us], dtype=torch.float64, device="cuda")
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comb_us_t = torch.tensor([comb_us], dtype=torch.float64, device="cuda")
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disp_bw_t = torch.tensor([disp_bw], dtype=torch.float64, device="cuda")
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comb_bw_t = torch.tensor([comb_bw], dtype=torch.float64, device="cuda")
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dist.all_reduce(disp_us_t, op=dist.ReduceOp.MAX, group=group)
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dist.all_reduce(comb_us_t, op=dist.ReduceOp.MAX, group=group)
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# Aggregate = sum across ranks; per-rank avg = sum / num_ranks. Also report
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# per-rank numbers to line up with NCCL-EP's `ep_bench.cu`, which prints the
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# rank's own bytes / its own elapsed time.
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disp_bw_agg = disp_bw_t.clone()
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comb_bw_agg = comb_bw_t.clone()
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dist.all_reduce(disp_bw_agg, op=dist.ReduceOp.SUM, group=group)
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dist.all_reduce(comb_bw_agg, op=dist.ReduceOp.SUM, group=group)
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if rank == 0:
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print(
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f"[bench LL] num_ranks={num_ranks} tokens={num_tokens} hidden={hidden} "
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f"num_experts={num_experts} num_topk={num_topk} warmup={warmup} iters={iters}",
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flush=True,
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)
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print(
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f" dispatch: {disp_us_t.item():.1f}us (max) "
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f"per_rank_bw={disp_bw_agg.item() / num_ranks:.2f} GB/s "
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f"agg_bw={disp_bw_agg.item():.2f} GB/s",
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flush=True,
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)
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print(
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f" combine : {comb_us_t.item():.1f}us (max) "
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f"per_rank_bw={comb_bw_agg.item() / num_ranks:.2f} GB/s "
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f"agg_bw={comb_bw_agg.item():.2f} GB/s",
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flush=True,
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)
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if __name__ == "__main__":
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try:
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main()
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finally:
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try:
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dist.destroy_process_group()
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except Exception:
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pass
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