# Copyright (c) Microsoft Corporation. # Licensed under the MIT License. """Multi-rank internode (HT) functional validation for mscclpp_ep. Launch on each node with (example: 2 nodes x 8 GPUs = 16 ranks): # on master (NODE_RANK=0): MASTER_ADDR= MASTER_PORT=29600 NODE_RANK=0 \ torchrun --nnodes=2 --nproc_per_node=8 \ --rdzv-backend=c10d --rdzv-endpoint=:29600 \ test/python/ep/test_internode_multirank.py # on worker (NODE_RANK=1): MASTER_ADDR= MASTER_PORT=29600 NODE_RANK=1 \ torchrun --nnodes=2 --nproc_per_node=8 \ --rdzv-backend=c10d --rdzv-endpoint=:29600 \ test/python/ep/test_internode_multirank.py Round-trip dispatch + combine using internode HT kernels across nodes. Set ``MSCCLPP_EP_BENCH=1`` to also run a post-correctness benchmark pass that times dispatch and combine **separately** with CUDA events. Reports per-phase latency (max across ranks) plus aggregate effective bandwidth (sum across ranks). Override iteration counts with ``MSCCLPP_EP_BENCH_WARMUP`` / ``MSCCLPP_EP_BENCH_ITERS`` and the bench problem size with ``MSCCLPP_EP_BENCH_TOKENS`` / ``_HIDDEN``. """ from __future__ import annotations import os import sys # Disable ProcessGroupNCCL's HeartbeatMonitor before importing torch.distributed. # It runs in a background thread polling the TCPStore; under mpirun, rank 0 # (the store server) can exit before non-zero ranks finish teardown, producing # noisy 'recvValue failed / Connection was likely closed' stack traces. os.environ.setdefault("TORCH_NCCL_ENABLE_MONITORING", "0") import torch import torch.distributed as dist def _detect_local_world_size(): """Number of GPUs per node (4 on GB200, 8 on H100/A100, etc.). Resolution order: 1. `MSCCLPP_EP_LOCAL_WORLD_SIZE` env var (matches the C++ side). 2. `LOCAL_WORLD_SIZE` (torchrun) or `OMPI_COMM_WORLD_LOCAL_SIZE` (mpirun). 3. `torch.cuda.device_count()` on the current host. """ for var in ("MSCCLPP_EP_LOCAL_WORLD_SIZE", "LOCAL_WORLD_SIZE", "OMPI_COMM_WORLD_LOCAL_SIZE"): v = os.environ.get(var) if v and int(v) > 0: return int(v) return max(1, torch.cuda.device_count()) def init_dist(): rank = int(os.environ["RANK"]) world_size = int(os.environ["WORLD_SIZE"]) local_world_size = _detect_local_world_size() local_rank = int(os.environ.get("LOCAL_RANK", rank % local_world_size)) torch.cuda.set_device(local_rank) dist.init_process_group( backend="nccl", world_size=world_size, rank=rank, device_id=torch.device(f"cuda:{local_rank}") ) return rank, world_size, local_rank, dist.new_group(list(range(world_size))) def inplace_unique(x: torch.Tensor, num_slots: int): assert x.dim() == 2 mask = x < 0 x_padded = x.masked_fill(mask, num_slots) bin_count = torch.zeros((x.size(0), num_slots + 1), dtype=x.dtype, device=x.device) bin_count.scatter_add_(1, x_padded, torch.ones_like(x_padded)) bin_count = bin_count[:, :num_slots] sorted_bin_count, sorted_bin_idx = torch.sort(bin_count, dim=-1, descending=True) sorted_bin_idx.masked_fill_(sorted_bin_count == 0, -1) sorted_bin_idx = torch.sort(sorted_bin_idx, descending=True, dim=-1).values x[:, :].fill_(-1) valid_len = min(num_slots, x.size(1)) x[:, :valid_len] = sorted_bin_idx[:, :valid_len] def main(): rank, num_ranks, local_rank, group = init_dist() from mscclpp import CommGroup import mscclpp.ep as ep ep_group = CommGroup(torch_group=group) NUM_MAX_NVL_PEERS = _detect_local_world_size() assert ( num_ranks % NUM_MAX_NVL_PEERS == 0 and num_ranks > NUM_MAX_NVL_PEERS ), f"expected >1 node with {NUM_MAX_NVL_PEERS} GPUs each, got num_ranks={num_ranks}" num_nodes = num_ranks // NUM_MAX_NVL_PEERS num_local_ranks = NUM_MAX_NVL_PEERS # Small settings for functional check import os as _os num_tokens = int(_os.environ.get("MSCCLPP_EP_BENCH_TOKENS", "128")) hidden = int(_os.environ.get("MSCCLPP_EP_BENCH_HIDDEN", "1024")) num_topk = int(_os.environ.get("MSCCLPP_EP_BENCH_TOPK", str(min(4, num_ranks)))) _experts_env = _os.environ.get("MSCCLPP_EP_BENCH_EXPERTS", "") num_experts = int(_experts_env) if _experts_env else num_ranks * 4 assert num_experts % num_ranks == 0 torch.manual_seed(0xA1B2 + rank) scores = torch.randn((num_tokens, num_experts), device="cuda", dtype=torch.float32).abs() + 1 topk_idx = torch.topk(scores, num_topk, dim=-1, sorted=False).indices topk_weights = torch.ones((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) rdma_rank_idx = rank_idx // num_local_ranks rdma_rank_idx.masked_fill_(rank_idx == -1, -1) inplace_unique(rdma_rank_idx, num_nodes) 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() num_tokens_per_rank = torch.empty((num_ranks,), dtype=torch.int, device="cuda") num_tokens_per_rdma_rank = torch.empty((num_nodes,), 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).values cnt = token_sel.sum().item() tokens = torch.sort(token_sel.to(torch.int), descending=True).indices tokens[:cnt] = torch.sort(tokens[:cnt]).values token_idx_in_rank[i][tokens[:cnt]] = torch.arange(cnt, dtype=torch.long, device="cuda") for i in range(num_nodes): num_tokens_per_rdma_rank[i] = (rdma_rank_idx == i).sum() token_idx_in_rank = token_idx_in_rank.T.contiguous().to(torch.int) is_token_in_rank = token_idx_in_rank >= 0 x = torch.ones((num_tokens, hidden), dtype=torch.bfloat16, device="cuda") * float(rank) moe = ep.MoECommunicator( comm=ep_group, num_experts=num_experts, hidden_size=hidden, topk=num_topk, max_tokens_per_rank=num_tokens, mode=ep.MoEMode.HIGH_THROUGHPUT, num_sms=int(os.environ.get("MSCCLPP_EP_NSM", "152")), nvl_chunked_send=int(os.environ.get("MSCCLPP_EP_NVL_SEND", "8")), nvl_chunked_recv=int(os.environ.get("MSCCLPP_EP_NVL_RECV", "256")), rdma_chunked_send=int(os.environ.get("MSCCLPP_EP_RDMA_SEND", "16")), rdma_chunked_recv=int(os.environ.get("MSCCLPP_EP_RDMA_RECV", "128")), ) if rank == 0: print( f"[cfg] num_nodes={num_nodes} num_ranks={num_ranks} num_tokens={num_tokens} " f"hidden={hidden} num_experts={num_experts} num_topk={num_topk}", flush=True, ) print( f"[rank {rank}] MoECommunicator created is_available={moe.is_available()} " f"is_internode={moe.is_internode_available()}", flush=True, ) assert moe.is_available() and moe.is_internode_available() assert moe.is_internode(), "expected the communicator to select the internode HT transport" dispatch_out, handle = moe.dispatch( x, topk_idx, topk_weights, ) recv_x = dispatch_out.tokens dist.barrier(group=group) assert recv_x.dim() == 2 and recv_x.size(1) == hidden local_experts = num_experts // num_ranks all_expert_counts = torch.empty((num_ranks, num_experts), dtype=num_tokens_per_expert.dtype, device="cuda") dist.all_gather_into_tensor(all_expert_counts, num_tokens_per_expert, group=group) expected_counts = all_expert_counts[:, rank * local_experts : (rank + 1) * local_experts].sum(dim=0).cpu().tolist() assert dispatch_out.layout.num_tokens_per_expert is not None actual_counts = [int(count) for count in dispatch_out.layout.num_tokens_per_expert] assert actual_counts == [int(count) for count in expected_counts] if rank == 0: print(f"[dispatch] OK (recv {recv_x.size(0)} tokens)", flush=True) # Keep the existing dispatch/combine phase guard for internode HT until the # backend wires a proper stream-dependency hand-off. torch.cuda.synchronize() dist.barrier(group=group) combined_x = moe.combine(recv_x, handle) num_dst = is_token_in_rank.sum(dim=1).to(torch.float32) expected = num_dst * float(rank) got = combined_x.float().mean(dim=1) diff = (got - expected).abs().max().item() max_exp = expected.abs().max().item() print(f"[combine r{rank}] max|got-expected|={diff:.4e} max|expected|={max_exp:.4e}", flush=True) # bf16 accumulator has 7-bit mantissa; intermediate partial sums can # round at ulp = max_exp * 2**-7. Use a tolerance that scales with magnitude. tol = max(1e-2, max_exp * (1.0 / 64)) assert diff <= tol, f"rank{rank}: combine mismatch max diff {diff} > tol {tol} (max_exp={max_exp})" dist.barrier(group=group) if rank == 0: print("PASS", flush=True) # ------------------------------------------------------------------ # Optional benchmark (enable with MSCCLPP_EP_BENCH=1). # ------------------------------------------------------------------ if os.environ.get("MSCCLPP_EP_BENCH", "0") != "1": return warmup = int(os.environ.get("MSCCLPP_EP_BENCH_WARMUP", "5")) iters = int(os.environ.get("MSCCLPP_EP_BENCH_ITERS", "20")) bench_tokens = int(os.environ.get("MSCCLPP_EP_BENCH_TOKENS", "4096")) bench_hidden = int(os.environ.get("MSCCLPP_EP_BENCH_HIDDEN", "7168")) # Allow overriding num_experts / num_topk for the bench phase to match # NCCL-EP's `ep_bench -a ht` defaults (256 experts, top-8). The functional # check above still uses the smaller (num_experts=num_ranks*4, topk=4) # configuration. bench_num_experts = int(os.environ.get("MSCCLPP_EP_BENCH_EXPERTS", str(num_experts))) bench_num_topk = int(os.environ.get("MSCCLPP_EP_BENCH_TOPK", str(num_topk))) if bench_num_experts % num_ranks != 0: if rank == 0: print( f"[bench] skip: num_experts={bench_num_experts} not divisible " f"by num_ranks={num_ranks}", flush=True ) return if bench_num_topk > bench_num_experts: if rank == 0: print(f"[bench] skip: topk={bench_num_topk} > experts={bench_num_experts}", flush=True) return scores_b = torch.randn((bench_tokens, bench_num_experts), device="cuda", dtype=torch.float32).abs() + 1 topk_idx_b = torch.topk(scores_b, bench_num_topk, dim=-1, sorted=False).indices topk_weights_b = torch.ones((bench_tokens, bench_num_topk), dtype=torch.float32, device="cuda") rank_idx_b = topk_idx_b // (bench_num_experts // num_ranks) rank_idx_b.masked_fill_(topk_idx_b == -1, -1) inplace_unique(rank_idx_b, num_ranks) rdma_rank_idx_b = rank_idx_b // num_local_ranks rdma_rank_idx_b.masked_fill_(rank_idx_b == -1, -1) inplace_unique(rdma_rank_idx_b, num_nodes) num_tokens_per_expert_b = torch.zeros((bench_num_experts,), dtype=torch.int, device="cuda") for i in range(bench_num_experts): num_tokens_per_expert_b[i] = (topk_idx_b == i).sum() num_tokens_per_rank_b = torch.empty((num_ranks,), dtype=torch.int, device="cuda") num_tokens_per_rdma_rank_b = torch.empty((num_nodes,), dtype=torch.int, device="cuda") token_idx_in_rank_b = torch.full((num_ranks, bench_tokens), -1, dtype=torch.long, device="cuda") for i in range(num_ranks): num_tokens_per_rank_b[i] = (rank_idx_b == i).sum() token_sel = (rank_idx_b == i).max(dim=-1).values cnt = token_sel.sum().item() tokens = torch.sort(token_sel.to(torch.int), descending=True).indices tokens[:cnt] = torch.sort(tokens[:cnt]).values token_idx_in_rank_b[i][tokens[:cnt]] = torch.arange(cnt, dtype=torch.long, device="cuda") for i in range(num_nodes): num_tokens_per_rdma_rank_b[i] = (rdma_rank_idx_b == i).sum() token_idx_in_rank_b = token_idx_in_rank_b.T.contiguous().to(torch.int) is_token_in_rank_b = token_idx_in_rank_b >= 0 x_b = torch.ones((bench_tokens, bench_hidden), dtype=torch.bfloat16, device="cuda") * float(rank) # Drive the benchmark through the public high-level API. The communicator # auto-selects internode HT when the RDMA size hint is non-zero. The first # (uncached) dispatch records routing layout on the returned handle; # subsequent dispatches reuse it via previous_handle, skipping host-side # layout computation. This isolates the on-GPU dispatch-kernel cost # (NCCL-EP ep_bench convention). moe = ep.MoECommunicator( comm=ep_group, num_experts=bench_num_experts, hidden_size=bench_hidden, topk=bench_num_topk, max_tokens_per_rank=bench_tokens, mode=ep.MoEMode.HIGH_THROUGHPUT, num_sms=int(os.environ.get("MSCCLPP_EP_NSM", "152")), nvl_chunked_send=int(os.environ.get("MSCCLPP_EP_NVL_SEND", "8")), nvl_chunked_recv=int(os.environ.get("MSCCLPP_EP_NVL_RECV", "256")), rdma_chunked_send=int(os.environ.get("MSCCLPP_EP_RDMA_SEND", "16")), rdma_chunked_recv=int(os.environ.get("MSCCLPP_EP_RDMA_RECV", "128")), ) assert moe.is_available() and moe.is_internode_available() assert moe.is_internode(), "expected the communicator to select the internode HT transport" # One uncached dispatch to build the cached routing layout on the handle. _handle0 = moe.dispatch(x_b, topk_idx_b, topk_weights_b)[1] def _dispatch_cached(): return moe.dispatch(x_b, topk_idx_b, topk_weights_b, previous_handle=_handle0) def _combine(dout): dispatch_out_, handle_ = dout moe.combine(dispatch_out_.tokens, handle_) # Warmup (full round-trip with the sync/barrier guard between phases, # matching the correctness-path invariant: internode combine must observe # the completed dispatch outputs before it launches). for _ in range(warmup): dout = _dispatch_cached() torch.cuda.synchronize() dist.barrier(group=group) _combine(dout) torch.cuda.synchronize() dist.barrier(group=group) # Time dispatch alone (cached mode -- skips the host-side layout computation). start_ev = torch.cuda.Event(enable_timing=True) end_ev = torch.cuda.Event(enable_timing=True) start_ev.record() dout = None for _ in range(iters): dout = _dispatch_cached() end_ev.record() torch.cuda.synchronize() disp_us = start_ev.elapsed_time(end_ev) * 1e3 / iters # Required guard before combine sees the dispatch outputs (see correctness # path's XXX note). Not included in either phase's timing. torch.cuda.synchronize() dist.barrier(group=group) # Time combine alone (reusing the same dispatch output each iter). start_ev.record() for _ in range(iters): _combine(dout) end_ev.record() torch.cuda.synchronize() comb_us = start_ev.elapsed_time(end_ev) * 1e3 / iters # Per-rank "send bytes" matches NCCL-EP's `ep_bench` accounting (`RDMA_send`): # bench_tokens * hidden * sizeof(bf16). Each rank ships its `bench_tokens` # input rows out (some replicated to multiple peers); NCCL-EP normalizes by # the input footprint, not by the recv-side fan-out. We use the same # convention here so `per_rank_bw` is directly comparable across stacks. bytes_one_way = bench_tokens * bench_hidden * x_b.element_size() # NCCL-EP `ep_bench` six-metric breakdown. # Send-side accounting follows NCCL-EP: count unique (token, dst_node) pairs. # `num_tokens_per_rdma_rank_b[n]` is exactly that count for node `n`. # Recv-side accounting: each rank reports `num_tokens_per_rank_b[r]` # (tokens it sends to dst rank `r`); an `all_to_all_single` lets every # rank read how many tokens each source rank sent to it. bytes_per_token = bench_hidden * x_b.element_size() local_node = rank // num_local_ranks nodes_unique = num_tokens_per_rdma_rank_b.to(torch.int64) total_send_tokens_local = int(nodes_unique.sum().item()) nvl_send_tokens_local = int(nodes_unique[local_node].item()) rdma_send_tokens_local = total_send_tokens_local - nvl_send_tokens_local # Replaced dist.all_to_all_single (NCCL socket transport fails with # NCCL_IB_DISABLE=1 internode) with all_gather_into_tensor + transpose, # which works on the same socket-NCCL setup the LL test uses. _send_row = num_tokens_per_rank_b.to(torch.int64).contiguous() _gathered = torch.empty(num_ranks * num_ranks, dtype=torch.int64, device="cuda") dist.all_gather_into_tensor(_gathered, _send_row, group=group) recv_from_src = _gathered.view(num_ranks, num_ranks)[:, rank].contiguous() src_node = torch.arange(num_ranks, device="cuda") // num_local_ranks remote_mask = (src_node != local_node).to(torch.int64) total_recv_tokens_local = int(recv_from_src.sum().item()) rdma_recv_tokens_local = int((recv_from_src * remote_mask).sum().item()) # Average per-rank token counts across ranks (matches NCCL-EP `Byte counts (per rank avg)`). counts_t = torch.tensor( [total_send_tokens_local, rdma_send_tokens_local, total_recv_tokens_local, rdma_recv_tokens_local], dtype=torch.float64, device="cuda", ) dist.all_reduce(counts_t, op=dist.ReduceOp.SUM, group=group) counts_avg = (counts_t / num_ranks).tolist() total_send_avg, rdma_send_avg, total_recv_avg, rdma_recv_avg = counts_avg total_send_bytes = total_send_avg * bytes_per_token rdma_send_bytes = rdma_send_avg * bytes_per_token total_recv_bytes = total_recv_avg * bytes_per_token rdma_recv_bytes = rdma_recv_avg * bytes_per_token nvl_send_bytes = total_send_bytes - rdma_send_bytes nvl_recv_bytes = total_recv_bytes - rdma_recv_bytes # Reduce timings: report min/avg/max and base BW on AVG to match NCCL-EP's # `ep_bench.cu` convention. disp_min_t = torch.tensor([disp_us], dtype=torch.float64, device="cuda") disp_avg_t = torch.tensor([disp_us], dtype=torch.float64, device="cuda") disp_max_t = torch.tensor([disp_us], dtype=torch.float64, device="cuda") comb_min_t = torch.tensor([comb_us], dtype=torch.float64, device="cuda") comb_avg_t = torch.tensor([comb_us], dtype=torch.float64, device="cuda") comb_max_t = torch.tensor([comb_us], dtype=torch.float64, device="cuda") dist.all_reduce(disp_min_t, op=dist.ReduceOp.MIN, group=group) dist.all_reduce(disp_avg_t, op=dist.ReduceOp.SUM, group=group) dist.all_reduce(disp_max_t, op=dist.ReduceOp.MAX, group=group) dist.all_reduce(comb_min_t, op=dist.ReduceOp.MIN, group=group) dist.all_reduce(comb_avg_t, op=dist.ReduceOp.SUM, group=group) dist.all_reduce(comb_max_t, op=dist.ReduceOp.MAX, group=group) disp_avg_us = disp_avg_t.item() / num_ranks comb_avg_us = comb_avg_t.item() / num_ranks disp_bw_per_rank = bytes_one_way / (disp_avg_us * 1e-6) / 1e9 comb_bw_per_rank = bytes_one_way / (comb_avg_us * 1e-6) / 1e9 # Six-metric BW (NCCL-EP convention). Combine reverses send<->recv: # in combine, this rank pushes back what it received in dispatch. disp_t_s = disp_avg_us * 1e-6 comb_t_s = comb_avg_us * 1e-6 d_send_total_bw = total_send_bytes / disp_t_s / 1e9 d_send_nvl_bw = nvl_send_bytes / disp_t_s / 1e9 d_send_rdma_bw = rdma_send_bytes / disp_t_s / 1e9 d_recv_total_bw = total_recv_bytes / disp_t_s / 1e9 d_recv_nvl_bw = nvl_recv_bytes / disp_t_s / 1e9 d_recv_rdma_bw = rdma_recv_bytes / disp_t_s / 1e9 c_send_total_bw = total_recv_bytes / comb_t_s / 1e9 c_send_nvl_bw = nvl_recv_bytes / comb_t_s / 1e9 c_send_rdma_bw = rdma_recv_bytes / comb_t_s / 1e9 c_recv_total_bw = total_send_bytes / comb_t_s / 1e9 c_recv_nvl_bw = nvl_send_bytes / comb_t_s / 1e9 c_recv_rdma_bw = rdma_send_bytes / comb_t_s / 1e9 if rank == 0: print( f"[bench internode HT] nodes={num_nodes} num_ranks={num_ranks} " f"tokens={bench_tokens} hidden={bench_hidden} " f"experts={bench_num_experts} topk={bench_num_topk} " f"warmup={warmup} iters={iters}", flush=True, ) print( f" dispatch: avg={disp_avg_us:.1f}us min={disp_min_t.item():.1f}us max={disp_max_t.item():.1f}us " f"per_rank_bw={disp_bw_per_rank:.2f} GB/s " f"agg_bw={disp_bw_per_rank * num_ranks:.2f} GB/s (BW @ avg time)", flush=True, ) print( f" send: total={d_send_total_bw:.2f} nvl={d_send_nvl_bw:.2f} rdma={d_send_rdma_bw:.2f} GB/s " f"recv: total={d_recv_total_bw:.2f} nvl={d_recv_nvl_bw:.2f} rdma={d_recv_rdma_bw:.2f} GB/s", flush=True, ) print( f" combine : avg={comb_avg_us:.1f}us min={comb_min_t.item():.1f}us max={comb_max_t.item():.1f}us " f"per_rank_bw={comb_bw_per_rank:.2f} GB/s " f"agg_bw={comb_bw_per_rank * num_ranks:.2f} GB/s (BW @ avg time)", flush=True, ) print( f" send: total={c_send_total_bw:.2f} nvl={c_send_nvl_bw:.2f} rdma={c_send_rdma_bw:.2f} GB/s " f"recv: total={c_recv_total_bw:.2f} nvl={c_recv_nvl_bw:.2f} rdma={c_recv_rdma_bw:.2f} GB/s", flush=True, ) print( f" byte counts (per rank avg): " f"total_send={total_send_bytes/1e6:.2f} MB ({total_send_avg:.0f} tok) " f"rdma_send={rdma_send_bytes/1e6:.2f} MB ({rdma_send_avg:.0f} tok) " f"total_recv={total_recv_bytes/1e6:.2f} MB ({total_recv_avg:.0f} tok) " f"rdma_recv={rdma_recv_bytes/1e6:.2f} MB ({rdma_recv_avg:.0f} tok)", flush=True, ) if __name__ == "__main__": try: main() except Exception: import traceback traceback.print_exc() sys.exit(1) finally: # Ordered shutdown: barrier so every rank reaches teardown before the # TCPStore server (rank 0) exits, then destroy the PG. Without this, # ProcessGroupNCCL's HeartbeatMonitor on non-zero ranks logs noisy # "recvValue failed / Connection was likely closed" stack traces. if dist.is_initialized(): try: dist.barrier() except Exception: pass dist.destroy_process_group()